The document proposes a streaming punctuation technique that leverages bidirectional context for continuous speech recognition. It introduces a novel approach of streaming punctuation that discards decoder segmentation and shifts punctuation decision making to a powerful Transformer model. Experimental results show that streaming punctuation improves segmentation accuracy by 13.9% and achieves an average BLEU score gain of 0.66 for downstream machine translation tasks.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...kevig
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is
also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIONA...ijnlc
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints.
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIO...kevig
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIONA...kevig
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints.
Transformer Models have taken over most of the Natural language Inference tasks. In recent
times they have proved to beat several benchmarks. Chunking means splitting the sentences into
tokens and then grouping them in a meaningful way. Chunking is a task that has gradually
moved from POS tag-based statistical models to neural nets using Language models such as
LSTM, Bidirectional LSTMs, attention models, etc. Deep neural net Models are deployed
indirectly for classifying tokens as different tags defined under Named Recognition Tasks. Later
these tags are used in conjunction with pointer frameworks for the final chunking task. In our
paper, we propose an Ensemble Model using a fine-tuned Transformer Model and a recurrent
neural network model together to predict tags and chunk substructures of a sentence. We
analyzed the shortcomings of the transformer models in predicting different tags and then
trained the BILSTM+CNN accordingly to compensate for the same.
Convolutional Neural Network and Feature Transformation for Distant Speech Re...IJECEIAES
In many applications, speech recognition must operate in conditions where there are some distances between speakers and the microphones. This is called distant speech recognition (DSR). In this condition, speech recognition must deal with reverberation. Nowadays, deep learning technologies are becoming the the main technologies for speech recognition. Deep Neural Network (DNN) in hybrid with Hidden Markov Model (HMM) is the commonly used architecture. However, this system is still not robust against reverberation. Previous studies use Convolutional Neural Networks (CNN), which is a variation of neural network, to improve the robustness of speech recognition against noise. CNN has the properties of pooling which is used to find local correlation between neighboring dimensions in the features. With this property, CNN could be used as feature learning emphasizing the information on neighboring frames. In this study we use CNN to deal with reverberation. We also propose to use feature transformation techniques: linear discriminat analysis (LDA) and maximum likelihood linear transformation (MLLT), on mel frequency cepstral coefficient (MFCC) before feeding them to CNN. We argue that transforming features could produce more discriminative features for CNN, and hence improve the robustness of speech recognition against reverberation. Our evaluations on Meeting Recorder Digits (MRD) subset of Aurora-5 database confirm that the use of LDA and MLLT transformations improve the robustness of speech recognition. It is better by 20% relative error reduction on compared to a standard DNN based speech recognition using the same number of hidden layers.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...kevig
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is
also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIONA...ijnlc
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints.
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIO...kevig
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIONA...kevig
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints.
Transformer Models have taken over most of the Natural language Inference tasks. In recent
times they have proved to beat several benchmarks. Chunking means splitting the sentences into
tokens and then grouping them in a meaningful way. Chunking is a task that has gradually
moved from POS tag-based statistical models to neural nets using Language models such as
LSTM, Bidirectional LSTMs, attention models, etc. Deep neural net Models are deployed
indirectly for classifying tokens as different tags defined under Named Recognition Tasks. Later
these tags are used in conjunction with pointer frameworks for the final chunking task. In our
paper, we propose an Ensemble Model using a fine-tuned Transformer Model and a recurrent
neural network model together to predict tags and chunk substructures of a sentence. We
analyzed the shortcomings of the transformer models in predicting different tags and then
trained the BILSTM+CNN accordingly to compensate for the same.
Convolutional Neural Network and Feature Transformation for Distant Speech Re...IJECEIAES
In many applications, speech recognition must operate in conditions where there are some distances between speakers and the microphones. This is called distant speech recognition (DSR). In this condition, speech recognition must deal with reverberation. Nowadays, deep learning technologies are becoming the the main technologies for speech recognition. Deep Neural Network (DNN) in hybrid with Hidden Markov Model (HMM) is the commonly used architecture. However, this system is still not robust against reverberation. Previous studies use Convolutional Neural Networks (CNN), which is a variation of neural network, to improve the robustness of speech recognition against noise. CNN has the properties of pooling which is used to find local correlation between neighboring dimensions in the features. With this property, CNN could be used as feature learning emphasizing the information on neighboring frames. In this study we use CNN to deal with reverberation. We also propose to use feature transformation techniques: linear discriminat analysis (LDA) and maximum likelihood linear transformation (MLLT), on mel frequency cepstral coefficient (MFCC) before feeding them to CNN. We argue that transforming features could produce more discriminative features for CNN, and hence improve the robustness of speech recognition against reverberation. Our evaluations on Meeting Recorder Digits (MRD) subset of Aurora-5 database confirm that the use of LDA and MLLT transformations improve the robustness of speech recognition. It is better by 20% relative error reduction on compared to a standard DNN based speech recognition using the same number of hidden layers.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Isolated word recognition using lpc & vector quantizationeSAT Journals
Abstract Speech recognition is always looked upon as a fascinating field in human computer interaction. It is one of the fundamental steps towards understanding human recognition and their behavior. This paper explicates the theory and implementation of Speech recognition. This is a speaker-dependent real time isolated word recognizer. The major logic used was to first obtain the feature vectors using LPC which was followed by vector quantization. The quantized vectors were then recognized by measuring the Minimum average distortion. All Speech Recognition systems contain Two Main Phases, namely Training Phase and Testing Phase. In the Training Phase, the Features of the words are extracted and during the recognition phase feature matching Takes place. The feature or the template thus extracted is stored in the data base, during the recognition phase the extracted features are compared with the template in the database. The features of the words are extracted by using LPC analysis. Vector Quantization is used for generating the code books. Finally the recognition decision is made based on the matching score. MATLAB will be used to implement this concept to achieve further understanding. Index Terms: Speech Recognition, LPC, Vector Quantization, and Code Book.
Arabic named entity recognition using deep learning approachIJECEIAES
Most of the Arabic Named Entity Recognition (NER) systems depend massively on external resources and handmade feature engineering to achieve state-of-the-art results. To overcome such limitations, we proposed, in this paper, to use deep learning approach to tackle the Arabic NER task. We introduced a neural network architecture based on bidirectional Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) and experimented with various commonly used hyperparameters to assess their effect on the overall performance of our system. Our model gets two sources of information about words as input: pre-trained word embeddings and character-based representations and eliminated the need for any task-specific knowledge or feature engineering. We obtained state-of-the-art result on the standard ANERcorp corpus with an F1 score of 90.6%.
QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITIONijma
The performance of various acoustic feature extraction methods has been compared in this work using
Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic
features are a series of vectors that represents the speech signals. They can be classified in either words or
sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic
vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector
extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction
(PLP) have also been used. These two methods closely resemble the human auditory system. These feature
vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes
are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to
investigate the nature of those acoustic features.
Bayesian distance metric learning and its application in automatic speaker re...IJECEIAES
This paper proposes state-of the-art Automatic Speaker Recognition System (ASR) based on Bayesian Distance Learning Metric as a feature extractor. In this modeling, I explored the constraints of the distance between modified and simplified i-vector pairs by the same speaker and different speakers. An approximation of the distance metric is used as a weighted covariance matrix from the higher eigenvectors of the covariance matrix, which is used to estimate the posterior distribution of the metric distance. Given a speaker tag, I select the data pair of the different speakers with the highest cosine score to form a set of speaker constraints. This collection captures the most discriminating variability between the speakers in the training data. This Bayesian distance learning approach achieves better performance than the most advanced methods. Furthermore, this method is insensitive to normalization compared to cosine scores. This method is very effective in the case of limited training data. The modified supervised i-vector based ASR system is evaluated on the NIST SRE 2008 database. The best performance of the combined cosine score EER 1.767% obtained using LDA200 + NCA200 + LDA200, and the best performance of Bayes_dml EER 1.775% obtained using LDA200 + NCA200 + LDA100. Bayesian_dml overcomes the combined norm of cosine scores and is the best result of the short2-short3 condition report for NIST SRE 2008 data.
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
CURVELET BASED SPEECH RECOGNITION SYSTEM IN NOISY ENVIRONMENT: A STATISTICAL ...ijcsit
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of
speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech
recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
Semantic Mask for Transformer Based End-to-End Speech RecognitionWhenty Ariyanti
The attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. Inspired by SpecAugment and BERT, this study proposed a semantic mask based regularization for training such kind of end-to-end (E2E) model. While this approach is applicable to the encoder-decoder framework with any type of Neural Network architecture, then study the transformer-based model for ASR and perform experiments on LibriSpeech 960h and TedLium2 dataset and achieve state-of-the-art performance on the test set in the scope of E2E models.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it
comes to high-performance chunking systems, transformer models have proved to be the state of the art
benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where
each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITIONijma
The performance of various acoustic feature extraction methods has been compared in this work using
Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic
features are a series of vectors that represents the speech signals. They can be classified in either words or
sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic
vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector
extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction
(PLP) have also been used. These two methods closely resemble the human auditory system. These feature
vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes
are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to
investigate the nature of those acoustic features.
QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITIONijma
The performance of various acoustic feature extraction methods has been compared in this work using
Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic
features are a series of vectors that represents the speech signals. They can be classified in either words or
sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic
vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector
extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction
(PLP) have also been used. These two methods closely resemble the human auditory system. These feature
vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes
are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to
investigate the nature of those acoustic features.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A Novel, Robust, Hierarchical, Text-Independent Speaker Recognition TechniqueCSCJournals
Automatic speaker recognition system is used to recognize an unknown speaker among several reference speakers by making use of speaker-specific information from their speech. In this paper, we introduce a novel, hierarchical, text-independent speaker recognition. Our baseline speaker recognition system accuracy, built using statistical modeling techniques, gives an accuracy of 81% on the standard MIT database and our baseline gender recognition system gives an accuracy of 93.795%. We then propose and implement a novel state-space pruning technique by performing gender recognition before speaker recognition so as to improve the accuracy/timeliness of our baseline speaker recognition system. Based on the experiments conducted on the MIT database, we demonstrate that our proposed system improves the accuracy over the baseline system by approximately 2%, while reducing the computational time by more than 30%.
Effect of Query Formation on Web Search Engine Resultskevig
Query in a search engine is generally based on natural language. A query can be expressed in more than
one way without changing its meaning as it depends on thinking of human being at a particular moment.
Aim of the searcher is to get most relevant results immaterial of how the query has been expressed. In the
present paper, we have examined the results of search engine for change in coverage and similarity of first
few results when a query is entered in two semantically same but in different formats. Searching has been
made through Google search engine. Fifteen pairs of queries have been chosen for the study. The t-test has
been used for the purpose and the results have been checked on the basis of total documents found,
similarity of first five and first ten documents found in the results of a query entered in two different
formats. It has been found that the total coverage is same but first few results are significantly different.
Investigations of the Distributions of Phonemic Durations in Hindi and Dogrikevig
Speech generation is one of the most important areas of research in speech signal processing which is now gaining a serious attention. Speech is a natural form of communication in all living things. Computers with the ability to understand speech and speak with a human like voice are expected to contribute to the development of more natural man-machine interface. However, in order to give those functions that are even closer to those of human beings, we must learn more about the mechanisms by which speech is produced and perceived, and develop speech information processing technologies that can generate a more natural sounding systems. The so described field of stud, also called speech synthesis and more prominently acknowledged as text-to-speech synthesis, originated in the mid eighties because of the emergence of DSP and the rapid advancement of VLSI techniques. To understand this field of speech, it is necessary to understand the basic theory of speech production. Every language has different phonetic alphabets and a different set of possible phonemes and their combinations.
For the analysis of the speech signal, we have carried out the recording of five speakers in Dogri (3 male and 5 females) and eight speakers in Hindi language (4 male and 4 female). For estimating the durational distributions, the mean of mean of ten instances of vowels of each speaker in both the languages has been calculated. Investigations have shown that the two durational distributions differ significantly with respect to mean and standard deviation. The duration of phoneme is speaker dependent. The whole investigation can be concluded with the end result that almost all the Dogri phonemes have shorter duration, in comparison to Hindi phonemes. The period in milli seconds of same phonemes when uttered in Hindi were found to be longer compared to when they were spoken by a person with Dogri as his mother tongue. There are many applications which are directly of indirectly related to the research being carried out. For instance the main application may be for transforming Dogri speech into Hindi and vice versa, and further utilizing this application, we can develop a speech aid to teach Dogri to children. The results may also be useful for synthesizing the phonemes of Dogri using the parameters of the phonemes of Hindi and for building large vocabulary speech recognition systems.
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Isolated word recognition using lpc & vector quantizationeSAT Journals
Abstract Speech recognition is always looked upon as a fascinating field in human computer interaction. It is one of the fundamental steps towards understanding human recognition and their behavior. This paper explicates the theory and implementation of Speech recognition. This is a speaker-dependent real time isolated word recognizer. The major logic used was to first obtain the feature vectors using LPC which was followed by vector quantization. The quantized vectors were then recognized by measuring the Minimum average distortion. All Speech Recognition systems contain Two Main Phases, namely Training Phase and Testing Phase. In the Training Phase, the Features of the words are extracted and during the recognition phase feature matching Takes place. The feature or the template thus extracted is stored in the data base, during the recognition phase the extracted features are compared with the template in the database. The features of the words are extracted by using LPC analysis. Vector Quantization is used for generating the code books. Finally the recognition decision is made based on the matching score. MATLAB will be used to implement this concept to achieve further understanding. Index Terms: Speech Recognition, LPC, Vector Quantization, and Code Book.
Arabic named entity recognition using deep learning approachIJECEIAES
Most of the Arabic Named Entity Recognition (NER) systems depend massively on external resources and handmade feature engineering to achieve state-of-the-art results. To overcome such limitations, we proposed, in this paper, to use deep learning approach to tackle the Arabic NER task. We introduced a neural network architecture based on bidirectional Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) and experimented with various commonly used hyperparameters to assess their effect on the overall performance of our system. Our model gets two sources of information about words as input: pre-trained word embeddings and character-based representations and eliminated the need for any task-specific knowledge or feature engineering. We obtained state-of-the-art result on the standard ANERcorp corpus with an F1 score of 90.6%.
QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITIONijma
The performance of various acoustic feature extraction methods has been compared in this work using
Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic
features are a series of vectors that represents the speech signals. They can be classified in either words or
sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic
vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector
extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction
(PLP) have also been used. These two methods closely resemble the human auditory system. These feature
vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes
are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to
investigate the nature of those acoustic features.
Bayesian distance metric learning and its application in automatic speaker re...IJECEIAES
This paper proposes state-of the-art Automatic Speaker Recognition System (ASR) based on Bayesian Distance Learning Metric as a feature extractor. In this modeling, I explored the constraints of the distance between modified and simplified i-vector pairs by the same speaker and different speakers. An approximation of the distance metric is used as a weighted covariance matrix from the higher eigenvectors of the covariance matrix, which is used to estimate the posterior distribution of the metric distance. Given a speaker tag, I select the data pair of the different speakers with the highest cosine score to form a set of speaker constraints. This collection captures the most discriminating variability between the speakers in the training data. This Bayesian distance learning approach achieves better performance than the most advanced methods. Furthermore, this method is insensitive to normalization compared to cosine scores. This method is very effective in the case of limited training data. The modified supervised i-vector based ASR system is evaluated on the NIST SRE 2008 database. The best performance of the combined cosine score EER 1.767% obtained using LDA200 + NCA200 + LDA200, and the best performance of Bayes_dml EER 1.775% obtained using LDA200 + NCA200 + LDA100. Bayesian_dml overcomes the combined norm of cosine scores and is the best result of the short2-short3 condition report for NIST SRE 2008 data.
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
CURVELET BASED SPEECH RECOGNITION SYSTEM IN NOISY ENVIRONMENT: A STATISTICAL ...ijcsit
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of
speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech
recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
Semantic Mask for Transformer Based End-to-End Speech RecognitionWhenty Ariyanti
The attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. Inspired by SpecAugment and BERT, this study proposed a semantic mask based regularization for training such kind of end-to-end (E2E) model. While this approach is applicable to the encoder-decoder framework with any type of Neural Network architecture, then study the transformer-based model for ASR and perform experiments on LibriSpeech 960h and TedLium2 dataset and achieve state-of-the-art performance on the test set in the scope of E2E models.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it
comes to high-performance chunking systems, transformer models have proved to be the state of the art
benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where
each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITIONijma
The performance of various acoustic feature extraction methods has been compared in this work using
Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic
features are a series of vectors that represents the speech signals. They can be classified in either words or
sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic
vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector
extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction
(PLP) have also been used. These two methods closely resemble the human auditory system. These feature
vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes
are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to
investigate the nature of those acoustic features.
QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITIONijma
The performance of various acoustic feature extraction methods has been compared in this work using
Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic
features are a series of vectors that represents the speech signals. They can be classified in either words or
sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic
vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector
extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction
(PLP) have also been used. These two methods closely resemble the human auditory system. These feature
vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes
are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to
investigate the nature of those acoustic features.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A Novel, Robust, Hierarchical, Text-Independent Speaker Recognition TechniqueCSCJournals
Automatic speaker recognition system is used to recognize an unknown speaker among several reference speakers by making use of speaker-specific information from their speech. In this paper, we introduce a novel, hierarchical, text-independent speaker recognition. Our baseline speaker recognition system accuracy, built using statistical modeling techniques, gives an accuracy of 81% on the standard MIT database and our baseline gender recognition system gives an accuracy of 93.795%. We then propose and implement a novel state-space pruning technique by performing gender recognition before speaker recognition so as to improve the accuracy/timeliness of our baseline speaker recognition system. Based on the experiments conducted on the MIT database, we demonstrate that our proposed system improves the accuracy over the baseline system by approximately 2%, while reducing the computational time by more than 30%.
Similar to STREAMING PUNCTUATION: A NOVEL PUNCTUATION TECHNIQUE LEVERAGING BIDIRECTIONAL CONTEXT FOR CONTINUOUS SPEECH RECOGNITION (20)
Effect of Query Formation on Web Search Engine Resultskevig
Query in a search engine is generally based on natural language. A query can be expressed in more than
one way without changing its meaning as it depends on thinking of human being at a particular moment.
Aim of the searcher is to get most relevant results immaterial of how the query has been expressed. In the
present paper, we have examined the results of search engine for change in coverage and similarity of first
few results when a query is entered in two semantically same but in different formats. Searching has been
made through Google search engine. Fifteen pairs of queries have been chosen for the study. The t-test has
been used for the purpose and the results have been checked on the basis of total documents found,
similarity of first five and first ten documents found in the results of a query entered in two different
formats. It has been found that the total coverage is same but first few results are significantly different.
Investigations of the Distributions of Phonemic Durations in Hindi and Dogrikevig
Speech generation is one of the most important areas of research in speech signal processing which is now gaining a serious attention. Speech is a natural form of communication in all living things. Computers with the ability to understand speech and speak with a human like voice are expected to contribute to the development of more natural man-machine interface. However, in order to give those functions that are even closer to those of human beings, we must learn more about the mechanisms by which speech is produced and perceived, and develop speech information processing technologies that can generate a more natural sounding systems. The so described field of stud, also called speech synthesis and more prominently acknowledged as text-to-speech synthesis, originated in the mid eighties because of the emergence of DSP and the rapid advancement of VLSI techniques. To understand this field of speech, it is necessary to understand the basic theory of speech production. Every language has different phonetic alphabets and a different set of possible phonemes and their combinations.
For the analysis of the speech signal, we have carried out the recording of five speakers in Dogri (3 male and 5 females) and eight speakers in Hindi language (4 male and 4 female). For estimating the durational distributions, the mean of mean of ten instances of vowels of each speaker in both the languages has been calculated. Investigations have shown that the two durational distributions differ significantly with respect to mean and standard deviation. The duration of phoneme is speaker dependent. The whole investigation can be concluded with the end result that almost all the Dogri phonemes have shorter duration, in comparison to Hindi phonemes. The period in milli seconds of same phonemes when uttered in Hindi were found to be longer compared to when they were spoken by a person with Dogri as his mother tongue. There are many applications which are directly of indirectly related to the research being carried out. For instance the main application may be for transforming Dogri speech into Hindi and vice versa, and further utilizing this application, we can develop a speech aid to teach Dogri to children. The results may also be useful for synthesizing the phonemes of Dogri using the parameters of the phonemes of Hindi and for building large vocabulary speech recognition systems.
May 2024 - Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Effect of Singular Value Decomposition Based Processing on Speech Perceptionkevig
Speech is an important biological signal for primary mode of communication among human being and also the most natural and efficient form of exchanging information among human in speech. Speech processing is the most important aspect in signal processing. In this paper the theory of linear algebra called singular value decomposition (SVD) is applied to the speech signal. SVD is a technique for deriving important parameters of a signal. The parameters derived using SVD may further be reduced by perceptual evaluation of the synthesized speech using only perceptually important parameters, where the speech signal can be compressed so that the information can be transformed into compressed form without losing its quality. This technique finds wide applications in speech compression, speech recognition, and speech synthesis. The objective of this paper is to investigate the effect of SVD based feature selection of the input speech on the perception of the processed speech signal. The speech signal which is in the form of vowels \a\, \e\, \u\ were recorded from each of the six speakers (3 males and 3 females). The vowels for the six speakers were analyzed using SVD based processing and the effect of the reduction in singular values was investigated on the perception of the resynthesized vowels using reduced singular values. Investigations have shown that the number of singular values can be drastically reduced without significantly affecting the perception of the vowels.
Identifying Key Terms in Prompts for Relevance Evaluation with GPT Modelskevig
Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4,
demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to
identify which specific terms in prompts positively or negatively impact relevance
evaluation with LLMs. We employed two types of prompts: those used in previous
research and generated automatically by LLMs. By comparing the performance of
these prompts in both few-shot and zero-shot settings, we analyze the influence of
specific terms in the prompts. We have observed two main findings from our study.
First, we discovered that prompts using the term ‘answer’ lead to more effective
relevance evaluations than those using ‘relevant.’ This indicates that a more direct
approach, focusing on answering the query, tends to enhance performance. Second,
we noted the importance of appropriately balancing the scope of ‘relevance.’ While
the term ‘relevant’ can extend the scope too broadly, resulting in less precise evaluations, an optimal balance in defining relevance is crucial for accurate assessments.
The inclusion of few-shot examples helps in more precisely defining this balance.
By providing clearer contexts for the term ‘relevance,’ few-shot examples contribute
to refine relevance criteria. In conclusion, our study highlights the significance of
carefully selecting terms in prompts for relevance evaluation with LLMs.
Identifying Key Terms in Prompts for Relevance Evaluation with GPT Modelskevig
Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4, demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to identify which specific terms in prompts positively or negatively impact relevance evaluation with LLMs. We employed two types of prompts: those used in previous research and generated automatically by LLMs. By comparing the performance of these prompts in both few-shot and zero-shot settings, we analyze the influence of specific terms in the prompts. We have observed two main findings from our study. First, we discovered that prompts using the term ‘answer’ lead to more effective relevance evaluations than those using ‘relevant.’ This indicates that a more direct approach, focusing on answering the query, tends to enhance performance. Second, we noted the importance of appropriately balancing the scope of ‘relevance.’ While the term ‘relevant’ can extend the scope too broadly, resulting in less precise evaluations, an optimal balance in defining relevance is crucial for accurate assessments. The inclusion of few-shot examples helps in more precisely defining this balance. By providing clearer contexts for the term ‘relevance,’ few-shot examples contribute to refine relevance criteria. In conclusion, our study highlights the significance of carefully selecting terms in prompts for relevance evaluation with LLMs.
In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word
sense disambiguator systems that, given a word appearing in a certain context, can identify the sense of
that word. In this paper we consider the problem of deciding whether same words contained in different
documents are related to the same meaning or are homonyms. Our goal is to improve the estimate of the
similarity of documents in which some words may be used with different meanings. We present three new
strategies for solving this problem, which are used to filter out homonyms from the similarity computation.
Two of them are intrinsically non-semantic, whereas the other one has a semantic flavor and can also be
applied to word sense disambiguation. The three strategies have been embedded in an article document
recommendation system that one of the most important Italian ad-serving companies offers to its customers.
Genetic Approach For Arabic Part Of Speech Taggingkevig
With the growing number of textual resources available, the ability to understand them becomes critical.
An essential first step in understanding these sources is the ability to identify the parts-of-speech in each
sentence. Arabic is a morphologically rich language, which presents a challenge for part of speech
tagging. In this paper, our goal is to propose, improve, and implement a part-of-speech tagger based on a
genetic algorithm. The accuracy obtained with this method is comparable to that of other probabilistic
approaches.
Rule Based Transliteration Scheme for English to Punjabikevig
Machine Transliteration has come out to be an emerging and a very important research area in the field of
machine translation. Transliteration basically aims to preserve the phonological structure of words. Proper
transliteration of name entities plays a very significant role in improving the quality of machine translation.
In this paper we are doing machine transliteration for English-Punjabi language pair using rule based
approach. We have constructed some rules for syllabification. Syllabification is the process to extract or
separate the syllable from the words. In this we are calculating the probabilities for name entities (Proper
names and location). For those words which do not come under the category of name entities, separate
probabilities are being calculated by using relative frequency through a statistical machine translation
toolkit known as MOSES. Using these probabilities we are transliterating our input text from English to
Punjabi.
Improving Dialogue Management Through Data Optimizationkevig
In task-oriented dialogue systems, the ability for users to effortlessly communicate with machines and computers through natural language stands as a critical advancement. Central to these systems is the dialogue manager, a pivotal component tasked with navigating the conversation to effectively meet user goals by selecting the most appropriate response. Traditionally, the development of sophisticated dialogue management has embraced a variety of methodologies, including rule-based systems, reinforcement learning, and supervised learning, all aimed at optimizing response selection in light of user inputs. This research casts a spotlight on the pivotal role of data quality in enhancing the performance of dialogue managers. Through a detailed examination of prevalent errors within acclaimed datasets, such as Multiwoz 2.1 and SGD, we introduce an innovative synthetic dialogue generator designed to control the introduction of errors precisely. Our comprehensive analysis underscores the critical impact of dataset imperfections, especially mislabeling, on the challenges inherent in refining dialogue management processes.
Document Author Classification using Parsed Language Structurekevig
Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship using grammatical structural information extracted using a statistical natural language parser. This paper provides a proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted from the statisticalnaturallanguage parserwere explored: all subtrees of some depth from any level; rooted subtrees of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the features into a lower dimensional space. Statistical experiments on these documents demonstrate that information from a statistical parser can, in fact, assist in distinguishing authors.
Rag-Fusion: A New Take on Retrieval Augmented Generationkevig
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multi-industry context.
Performance, Energy Consumption and Costs: A Comparative Analysis of Automati...kevig
The common practice in Machine Learning research is to evaluate the top-performing models based on their performance. However, this often leads to overlooking other crucial aspects that should be given careful consideration. In some cases, the performance differences between various approaches may be insignificant, whereas factors like production costs, energy consumption, and carbon footprint should be taken into account. Large Language Models (LLMs) are widely used in academia and industry to address NLP problems. In this study, we present a comprehensive quantitative comparison between traditional approaches (SVM-based) and more recent approaches such as LLM (BERT family models) and generative models (GPT2 and LLAMA2), using the LexGLUE benchmark. Our evaluation takes into account not only performance parameters (standard indices), but also alternative measures such as timing, energy consumption and costs, which collectively contribute to the carbon footprint. To ensure a complete analysis, we separately considered the prototyping phase (which involves model selection through training-validation-test iterations) and the in-production phases. These phases follow distinct implementation procedures and require different resources. The results indicate that simpler algorithms often achieve performance levels similar to those of complex models (LLM and generative models), consuming much less energy and requiring fewer resources. These findings suggest that companies should consider additional considerations when choosing machine learning (ML) solutions. The analysis also demonstrates that it is increasingly necessary for the scientific world to also begin to consider aspects of energy consumption in model evaluations, in order to be able to give real meaning to the results obtained using standard metrics (Precision, Recall, F1 and so on).
Evaluation of Medium-Sized Language Models in German and English Languagekevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering, which requires models to provide elaborate answers without external document retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.
IMPROVING DIALOGUE MANAGEMENT THROUGH DATA OPTIMIZATIONkevig
In task-oriented dialogue systems, the ability for users to effortlessly communicate with machines and
computers through natural language stands as a critical advancement. Central to these systems is the
dialogue manager, a pivotal component tasked with navigating the conversation to effectively meet user
goals by selecting the most appropriate response. Traditionally, the development of sophisticated dialogue
management has embraced a variety of methodologies, including rule-based systems, reinforcement
learning, and supervised learning, all aimed at optimizing response selection in light of user inputs. This
research casts a spotlight on the pivotal role of data quality in enhancing the performance of dialogue
managers. Through a detailed examination of prevalent errors within acclaimed datasets, such as
Multiwoz 2.1 and SGD, we introduce an innovative synthetic dialogue generator designed to control the
introduction of errors precisely. Our comprehensive analysis underscores the critical impact of dataset
imperfections, especially mislabeling, on the challenges inherent in refining dialogue management
processes.
Document Author Classification Using Parsed Language Structurekevig
Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the
text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used,
for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern
times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of
using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship
using grammatical structural information extracted using a statistical natural language parser. This paper provides a
proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist
Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted
of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the
features into a lower dimensional space. Statistical experiments on these documents demonstrate that information
from a statistical parser can, in fact, assist in distinguishing authors.
RAG-FUSION: A NEW TAKE ON RETRIEVALAUGMENTED GENERATIONkevig
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product
information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots,
but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines
RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal
scores and fusing the documents and scores. Through manually evaluating answers on accuracy,
relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and
comprehensive answers due to the generated queries contextualizing the original query from various
perspectives. However, some answers strayed off topic when the generated queries' relevance to the
original query is insufficient. This research marks significant progress in artificial intelligence (AI) and
natural language processing (NLP) applications and demonstrates transformations in a global and multiindustry context
Performance, energy consumption and costs: a comparative analysis of automati...kevig
The common practice in Machine Learning research is to evaluate the top-performing models based on their
performance. However, this often leads to overlooking other crucial aspects that should be given careful
consideration. In some cases, the performance differences between various approaches may be insignificant, whereas factors like production costs, energy consumption, and carbon footprint should be taken into
account. Large Language Models (LLMs) are widely used in academia and industry to address NLP problems. In this study, we present a comprehensive quantitative comparison between traditional approaches
(SVM-based) and more recent approaches such as LLM (BERT family models) and generative models (GPT2 and LLAMA2), using the LexGLUE benchmark. Our evaluation takes into account not only performance
parameters (standard indices), but also alternative measures such as timing, energy consumption and costs,
which collectively contribute to the carbon footprint. To ensure a complete analysis, we separately considered the prototyping phase (which involves model selection through training-validation-test iterations) and
the in-production phases. These phases follow distinct implementation procedures and require different resources. The results indicate that simpler algorithms often achieve performance levels similar to those of
complex models (LLM and generative models), consuming much less energy and requiring fewer resources.
These findings suggest that companies should consider additional considerations when choosing machine
learning (ML) solutions. The analysis also demonstrates that it is increasingly necessary for the scientific
world to also begin to consider aspects of energy consumption in model evaluations, in order to be able to
give real meaning to the results obtained using standard metrics (Precision, Recall, F1 and so on).
EVALUATION OF MEDIUM-SIZED LANGUAGE MODELS IN GERMAN AND ENGLISH LANGUAGEkevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks
clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six
billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative
question answering, which requires models to provide elaborate answers without external document
retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results
show that combining the best answers from different MLMs yielded an overall correct answer rate of
82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B
parameters, which highlights the importance of using appropriate training data for fine-tuning rather than
solely relying on the number of parameters. More fine-grained feedback should be used to further improve
the quality of answers. The open source community is quickly closing the gap to the best commercial
models.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
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STREAMING PUNCTUATION: A NOVEL PUNCTUATION TECHNIQUE LEVERAGING BIDIRECTIONAL CONTEXT FOR CONTINUOUS SPEECH RECOGNITION
1. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
DOI: 10.5121/ijnlc.2022.11601 1
STREAMING PUNCTUATION:
A NOVEL PUNCTUATION TECHNIQUE
LEVERAGING BIDIRECTIONAL CONTEXT FOR
CONTINUOUS SPEECH RECOGNITION
Piyush Behre, Sharman Tan, Padma Varadharajan and Shuangyu Chang
Microsoft Corporation
ABSTRACT
While speech recognition Word Error Rate (WER) has reached human parity for English, continuous
speech recognition scenarios such as voice typing and meeting transcriptions still suffer from segmentation
and punctuation problems, resulting from irregular pausing patterns or slow speakers. Transformer
sequence tagging models are effective at capturing long bi-directional context, which is crucial for
automatic punctuation. Automatic Speech Recognition (ASR) production systems, however, are constrained
by real-time requirements, making it hard to incorporate the right context when making punctuation
decisions. Context within the segments produced by ASR decoders can be helpful but limiting in overall
punctuation performance for a continuous speech session. In this paper, we propose a streaming approach
for punctuation or re-punctuation of ASR output using dynamic decoding windows and measure its impact
on punctuation and segmentation accuracy across scenarios. The new system tackles over-segmentation
issues, improving segmentation F0.5-score by 13.9%. Streaming punctuation achieves an average BLEU-
score improvement of 0.66 for the downstream task of Machine Translation (MT).
KEYWORDS
automatic punctuation, automatic speech recognition, re-punctuation, speech segmentation
1. INTRODUCTION
Our hybrid Automatic Speech Recognition (ASR) generates punctuation with two systems
working together. First, the decoder generates text segments and passes them to the Display Post
Processor (DPP). The DPP system then applies punctuation to these text segments.
This two-stage setup works well for single-shot use cases such as voice assistant or voice search
but performs poorly on long-form dictation. A dictation session typically comprises many
spoken-form text segments generated by the decoder. Decoder features such as speaker pause
duration determine the segment boundaries. The punctuation model in DPP then punctuates each
of those segments. Without cross-segment look-ahead or the ability to correct previously
finalized results, the punctuation model functions within the boundaries of each provided text
segment. Consequently, punctuation model performance is highly dependent on the quality of
text segments generated by the decoder.
Our past investments have focused on both systems independently - (1) improving decoder
segmentation using look-ahead-based acoustic-linguistic features [32] and (2) using neural
network architectures to punctuate in DPP. As measured by Punctuation-F1 scores, these
investments have improved our punctuation quality. However, over-segmentation in cases of
slow speakers or irregular pausing is still prominent.
2. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
2
With streaming punctuation, we explore a system that discards decoder segmentation, instead
shifting punctuation decision making towards a powerful long-context Transformer-based
punctuation model. Rather than preliminary text segments, this system emits well-formed
punctuated sentences, which is much more desirable for downstream tasks like translation of
ASR output. The proposed architecture also satisfies real-time latency constraints for commercial
ASR use cases.
Many works have demonstrated that leveraging prosodic features and audio inputs can improve
punctuation quality [1, 2, 3]. However, as we show in our experiments, misleading pauses may
significantly undermine punctuation quality and encourage overly aggressive punctuation. This is
especially true in scenarios such as dictation, in which users pause often and unintentionally. Our
work demonstrates that text-only streaming punctuation is robust to over-segmentation from
irregular pauses and slow speakers.
We make the following key contributions: (1) We introduce a novel streaming punctuation
approach to punctuate and re-punctuate ASR outputs, as described in section 3, (2) we
demonstrate streaming punctuation's robustness to model architecture choices through
experiments described in section 5, and (3) we achieve not only gains in punctuation quality but
also significant downstream Bilingual Evaluation Understudy (BLEU) score gains on Machine
Translation (MT) for a set of languages, as demonstrated in section 6.
2. RELATED WORK
Decoder segmentation conventionally involves using predefined silence timeouts or Voice
Activity Detectors (VADs) to identify end of segment boundaries in text sequences. A separate
punctuation system then applies punctuation and capitalization on these segments. Advancements
in end-of-segment boundary detection include the addition of model-based techniques based on
acoustic features [29, 30, 31] as well as acoustic-linguistic features [32]. Prior work has also
explored end-to-end systems for end-of-segment boundary detection, jointly segmenting and
decoding audio inputs with a focus on long-form ASR [33]. In this paper, we explore a system
that does away with decoder segmentation boundaries and instead shifts punctuation decisions
towards a powerful long-context Transformer-based punctuation model. As a baseline
comparison, we use a conventional VAD-based system to generate decoder segments that we
later feed into a downstream LSTM punctuation model.
Approaches to punctuation restoration have evolved to capture surrounding context more
effectively. Early sequence labelling approaches for punctuation restoration used n-grams to
capture context [4]. However, this simple approach becomes unscalable as n grows large, and
does not generalize well to unseen data. This approach limits the amount of context that can be
used in punctuation prediction.
Classical machine learning approaches such as conditional random fields (CRFs) [5, 6, 7],
maximum entropy models [8], and hidden Markov models (HMMs) [9] model more complex
features by leveraging manual feature engineering. This manual process is slow and cumbersome,
and the quality of these features is dependent on feature engineers. These dependencies challenge
the effectiveness of these classical approaches.
Neural approaches mostly displaced manual feature engineering, opting instead to learn more
complex features through deep neural models. Recurrent neural networks (RNNs), specifically
Gated Recurrent Units (GRUs) and bidirectional Long Short-Term Memory (LSTM) networks,
have advanced natural language processing (NLP) and punctuation restoration by specifically
modelling long-term dependencies in the text [10, 11, 12, 13, 14]. Prior works have also
3. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
3
successfully used LSTMs with CRF layers [15, 16]. Most recently, using Transformers [17] and
especially pre-trained embeddings from models such as Bidirectional Encoder Representations
from Transformers (BERT) [18] has significantly advanced quality across natural language
processing (NLP) tasks. By leveraging attention mechanisms and more complex model
architectures, Transformers can better capture bidirectional long-range text dependencies for
punctuation restoration [19, 20, 21, 22, 23, 24].
3. PROPOSED METHOD
3.1. Punctuation Model
We frame punctuation prediction as a neural sequence tagging problem. Figure 1 illustrates the
end-to-end punctuation tagging and tag application process. We first tokenize the raw input text
segment as a sequence of byte-pair encoding (BPE) tokens and pass this through a transformer
encoder. Next, a punctuation token classification head, consisting of a dropout layer and a fully
connected layer, generates token-level punctuation tags. Finally, we convert the token-level tags
to word-level tags and generate the final punctuated text by appending each tag-specified
punctuation symbol to the corresponding word in the input segment.
Figure 1. Punctuation tagging model using transformer encoder
Figure 2. LSTM punctuation tagging model workflow with 4-word look-ahead enabled via <pad> input
and output tokens
4. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
4
To demonstrate the robustness of streaming punctuation to different model architectures, we also
conduct experiments with an LSTM tagging model with look-ahead rather than more powerful
transformer-based models. Example text inputs to and tag outputs from the LSTM model with
four-word look-ahead are illustrated in Figure 2 above. As the figure shows, we pad the model’s
inputs and outputs by tokens corresponding to specified look-ahead value to enable predictions
with limited right context available. Once the model produces tags, we strip off the pad tokens to
obtain a one-to-one mapping between each non-pad input token and each punctuation tag output.
3.2. Establishing Importance of Context Across Segment Boundaries
We first performed a study to better understand the importance and limitations of context as used
in punctuating speech recognition output. Typically, ASR systems use silence-based timeouts or
voice activity detection (VAD) to produce decoder segments. For slow speakers and users
speaking with irregular pauses, this system can easily segment too aggressively. Similarly for fast
speakers, the decoder segmentation may under-segment, resulting in lengthy segments. In an
under-segmenting system, the segmentation eventually happens based on a pre-determined
segment length timeout (e.g., 40 seconds).
Our baseline for this preliminary experiment was a system that punctuates only based on current
context information. We considered two candidate systems for comparison. Left-segment context
(LC) system considers previous segment appended as left context to the current segment but does
not change the previous segment punctuation already produced. Right-segment context (RC)
system considers the next segment appended as right context to the current segment, and only
applies punctuation to the current segment. Table 1 presents the results of this preliminary
experiment.
Table 1. Segmentation results with varying cross-segment context on a mixed set
Context setup
Segmentation
P R F1 F1-gain F0.5
F0.5-
gain
In-segment context
Left-segment context
Right-segment context
64
64
80
82
84
69
72
73
74
1.4%
2.8%
67
67
78
0.4%
15.8%
Lower-precision and higher-recall is indicative of a system with over-segmentation problems.
The LC system only slightly benefits from the additional left context and segmentation F0.5
improves by only 0.4 percent. However, the RC system does a much better job in tackling the
over-segmentation problem. This system improves segmentation F0.5 by 15.8 percent, inverting
the precision-recall tilt, which is much more desirable by our users. The RC system described
here is not deployable in a streaming ASR service. However, this formed the basis of our
streaming decoder approach to apply punctuation which we discuss next.
3.3. Streaming Decoder for Punctuation
Hybrid ASR systems often define segmentation boundaries using predefined silence thresholds.
However, for human2machine scenarios like dictation, pauses do not necessarily indicate ideal
segmentation boundaries for the ASR system. In our experience, users pause at unpredictable
moments as they stop to think. All A1-4 segments in Table 2 are possible; each is a valid
sentence with correct punctuation. Even with a punctuation model, if A4 is the user’s intended
sentence, all A1-3 would be incorrect. For dictation users, this system would produce over-
5. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
5
segmentation. To solve this issue, we must incorporate the right context across segment
boundaries.
Table 2. Examples of possible segments generated by ASR
Id Segment
Segment A1 It can happen.
Segment A2 It can happen in New York.
Segment A3 It can happen in New York City.
Segment A4 It can happen in New York City, right?
Our solution is a streaming punctuation system. The key is to emit complete sentences only after
detecting the beginning of a new sentence. At each step, we punctuate text within a dynamic
decoding window. This window consists of a buffer for which the system has not yet detected a
sentence boundary as well as the new incoming segment. When the system detects at least one
sentence boundary within the dynamic decoding window, we emit all complete sentences and
reserve any remaining text as the new buffer. This process is illustrated in Figure 3 below.
Processing and punctuating each of the input segments separately and independently is
problematic, as bad decoder segmentation leads to mid-sentence segmentation. As demonstrated
in the punctuation finalized output column, streaming punctuation effectively enables punctuation
decision-making across sentence boundaries with just enough surrounding context available.
Streaming punctuation is a powerful way to improve final punctuation results, regardless of the
decoder segmentation mechanism that is used to produce the input segments.
Figure 3. Dynamic decoding window for streaming punctuation
This strategy discards the original decoder boundary and decides the sentence boundary purely
based on linguistic features. A powerful transformer model that captures the long context well is
ideal for this strategy, as dynamic windows ensure that we incorporate enough left and right
context before finalizing punctuation. Our approach also meets real-time requirements for ASR
without incurring additional user-perceived latency, owing to the continual generation of
hypothesis buffers within the same latency constraints. An improvement to this system would be
to use a prosody-aware punctuation model that captures both acoustic and linguistic features.
That would be a way to re-capture the acoustic cues that we lose by discarding the original
segments. However, prosody-aware punctuation models may cause regressions in scenarios such
as dictation in which users’ pauses do not necessarily correspond to the presence of mid-sentence
or end-of-sentence punctuation.
6. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
6
4. DATA PROCESSING PIPELINE
4.1. Datasets
We use public datasets from various domains to ensure a good mix of conversational and written-
form data. Table 3 shows the word count distributions by percentage among the sets.
OpenWebText [25]: This dataset consists of web content from URLs shared on Reddit with at
least three upvotes. This is our primary source of form written form (human2machine) data.
Stack Exchange: This dataset consists of user-contributed content on the Stack Exchange
network. As this dataset consists of questions and answers, it is primarily of conversational
(human2human) flavour.
OpenSubtitles2016 [26]: This dataset consists of movie and TV subtitles. This is also primarily
conversational (human2human).
Multimodal Aligned Earnings Conference (MAEC) [27]: This dataset consists of transcribed
earnings calls based on S&P 1500 companies. Typically, each earnings call consists of a section
of prepared remarks (human2group), followed by a Q&A section.
National Public Radio (NPR) Podcast: This dataset consists of transcribed NPR Podcast
episodes. Typically, this consists of conversations between two to three individuals.
Table 3. Data distribution by number of words per training dataset
Dataset Distribution
OpenWebText 52.8%
Stack Exchange 31.5%
OpenSubtitles2016 7.6%
MAEC 6.7%
NPR Podcast 1.4%
4.2. Data Processing
As described in Section 3.1, the transformer sequence tagging model takes spoken-form
unpunctuated text as input and outputs a sequence of token-level tags signifying the punctuation
to append to the corresponding input word. All datasets consist of punctuated written-form
paragraphs, and we process them to generate spoken-form input text and corresponding output
punctuation tag sequences for training.
To preserve the original context, we keep the original paragraph breaks in the datasets and use
each paragraph as a training row. We first clean and filter the sets, removing symbols apart from
alphanumeric, punctuation, and necessary mid-word symbols such as hyphens. To generate
spoken-form unpunctuated data, we strip off all punctuation from the written-form paragraphs
and use a Weighted Finite-State Transducers (WFST) based text normalization system to
generate spoken-form paragraphs. During text normalization, we preserve alignments between
each written-form word and its spoken form. We then use these alignments and the original
punctuated display text to generate ground truth punctuation tags corresponding to the spoken-
form text.
7. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
7
We set aside 10 percent or at most fifty thousand paragraphs from each set for validation and use
the remaining data for training.
4.3. Tag Classes
We define four tag categories: comma, period, question mark, and ‘O’ for no punctuation. Each
punctuation tag represents the punctuation symbol that appears appended to the corresponding
text token. When we convert input word sequences into BPE sequences, we attach the tags only
to the last BPE token for each word. We tag the rest of the tokens with ‘O’. For punctuation
symbols other than comma, period, and question mark, we either convert them into the
appropriate supported symbols (comma, period, or question mark) or remove those unsupported
symbols entirely based on simple heuristics
.
5. EXPERIMENTS
5.1. Test Sets
We evaluate our punctuation model performance across various scenarios using private and
public test sets. Each set contains long-form audio and corresponding written-form transcriptions
with number formatting, capitalization, and punctuation. Starting from audio rather than text is
critical to highlight the challenges associated with irregular pauses or slow speakers. This
prohibits us from using the text-only International Conference on Spoken Language Translation
(IWSLT) 2011 TED Talks corpus, typically used for reporting punctuation model performance.
Dictation (Dict-100): This internal set consists of one hundred sessions of long-form dictation
ASR outputs and corresponding human transcriptions. On average, each session is 180 seconds
long. Multiple judges process these sessions to generate the reference transcription in spoken and
written form.
MAEC: 10 hours of test data taken from the MAEC corpus, containing transcribed earnings
calls. This corresponds to ten earnings calls, each an hour long. Transcribers remove disfluencies,
false starts, and repetitions for this set to make it more readable.
European Parliament (EP-100): This dataset contains one hundred English sessions scraped
from European Parliament Plenary [34] videos. This dataset already contains English
transcriptions, and human annotators provided corresponding translations into seven other
languages. We use the source English transcriptions to measure segmentation and punctuation
improvements. We use the translation reference to measure BLEU scores for the downstream
task of Machine Translation.
NPR Podcast (NPR-76): 20 hours of test data from transcribed NPR Podcast episodes. On
average, each session is 15 minutes long.
5.2. Experimental Setup
Our baseline system primarily uses Voice Activity Detection (VAD) based segmentation with a
silence-based timeout threshold of 500ms. When VAD does not trigger, the system applies a
segmentation at 40 seconds. The streaming punctuation system receives the input from the
baseline system but can delay finalizing punctuation decisions until it detects the beginning of a
new sentence.
8. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
8
We hypothesize that streaming punctuation outperforms the baseline system. We evaluate our
hypothesis on LSTM and transformer punctuation tagging models. For the LSTM tagging model,
we trained a 1-layer LSTM with 512-dimension word embeddings and 1024 hidden units. We
used a look-ahead of four words, providing limited right context for better punctuation decisions.
For the transformer tagging model, we trained a 12-layer transformer with sixteen attention
heads, 1024-dimension word embeddings, 4096-dimension fully connected layers, and 8-
dimension layers projecting from the transformer encoder to the decoder that maps to the tag
classes.
We use 32 thousand BPE units for model input vocabulary. We limited training paragraph
lengths to 250 BPE tokens and trimmed each to its last complete sentence. We trained all the
models to convergence.
6. RESULTS AND DISCUSSION
We compare the results of our baseline (BL) and streaming (ST) punctuation systems on (1) the
LSTM tagging model and (2) the Transformer tagging model. As expected, Transformers
outperform LSTMs for this task. Here we evaluate our hypothesis for both model types to
establish the effectiveness and robustness of our proposed system. For LSTM tagging models,
BL-LSTM refers to the baseline system, and ST-LSTM refers to the streaming punctuation
system. Similarly, for Transformer tagging models, BL-Transformer refers to the baseline
system, and ST-Transformer refers to the streaming punctuation system.
6.1. Punctuation and Segmentation Accuracy
We measure and report punctuation accuracy with word-level precision (P), recall (R), and F1-
score. Table 4 summarizes punctuation metrics measured and aggregated over three punctuation
categories: period, question mark, and comma.
Our customers consistently prefer higher precision (system only acting when confident) over
higher recall (system punctuating generously). Punctuation-F1 does not fully capture this
preference. Customers also place higher importance on correctly detecting sentence boundaries
over commas. We, therefore, propose segmentation-F0.5 as a primary metric for this and future
sentence segmentation work. The segmentation metric ignores commas and treats periods and
question marks interchangeably, thus only measuring the quality of sentence boundaries. Table 5
summarizes segmentation metrics.
Although our target scenario was long-form dictation (human2machine), we found this technique
equally beneficial for conversational (human2human) and broadcast (human2group) scenarios,
establishing its robustness across applications. On average, the ST-Transformer system has a
Segmentation-F0.5 gain of 13.9 percent and a Punctuation-F1 gain of 4.3 percent over the BL-
Transformer system. Similarly, the ST-LSTM system has a Segmentation-F0.5 improvement of
12.2 percent and a Punctuation-F1 improvement of 2.1 percent over the BL-LSTM system. These
results support our hypothesis that our streaming punctuation technique is effective and robust to
different model architectures.
6.2. Downstream Task: Machine Translation
We measure the impact of segmentation and punctuation improvements on the downstream task
of MT. Higher quality punctuation leads to translation BLEU gains for all seven target languages,
as summarized in Table 6. The ST-Transformer system achieves the best results across all seven
9. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
9
target languages. On average, the ST-Transformer system has a BLEU score improvement of
0.66 over the BL-Transformer and wins for all target languages. Similarly, the ST-LSTM system
has a BLEU score improvement of 0.33 over the BL-LSTM system and wins for five out of seven
target languages. These results support our hypothesis.
Table 4. Punctuation results
Test
Set
Model
PERIOD Q-MARK COMMA OVERALL F1-
Gain
P R F1 P R F1 P R F1 P R F1
Dict-
100
BL-LSTM
ST-LSTM
64
77
71
63
67
69
47
67
88
71
61
69
62
60
52
52
57
56
63
68
61
57
61
62 0.6%
BL-Transf
ST-Transf
69
81
76
71
72
76
50
82
88
82
64
82
68
69
52
51
59
59
68
74
63
60
65
67 2.9%
MAEC
BL-LSTM
ST-LSTM
68
77
79
70
73
73
46
65
44
45
45
54
63
60
50
51
56
55
65
68
63
60
64
64 0.0%
BL-Transf
ST-Transf
71
80
80
78
75
79
50
69
50
46
50
56
65
65
49
48
56
55
67
72
63
62
65
66 2.4%
EP-
100
BL-LSTM
ST-LSTM
56
70
71
62
63
66
64
69
62
55
63
61
55
57
47
49
51
53
56
63
58
55
56
59 4.2%
BL-Transf
ST-Transf
58
70
76
71
66
71
58
76
70
70
64
73
57
59
49
51
53
55
57
64
61
60
59
62 5.8%
NPR-
76
BL-LSTM
ST-LSTM
72
82
71
71
72
76
71
76
66
69
69
73
65
65
58
59
61
62
69
74
65
66
67
70 4.0%
BL-Transf
ST-Transf
76
87
77
79
76
83
76
81
70
75
73
78
68
70
60
61
64
65
72
79
69
71
71
75 6.0%
10. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
10
Table 5. Segmentation results
Test
Set
Model
Segmentation
P R F1 F1-gain F0.5
F0.5-
gain
Dict-
100
BL-LSTM
ST-LSTM
62
74
68
60
65
66 1.5%
63
71 12.0%
BL-Transformer
ST-Transformer
66
79
74
69
70
73 4.3%
67
77 13.8%
MAEC
BL-LSTM
ST-LSTM
66
76
76
68
71
72 1.4%
68
74 9.5%
BL-Transformer
ST-Transformer
69
79
77
75
73
77 5.5%
70
78 10.9%
EP-
100
BL-LSTM
ST-LSTM
53
66
67
58
59
62 5.1%
55
64 16.1%
BL-Transformer
ST-Transformer
54
67
72
68
62
68 9.7%
57
67 18.2%
NPR-
76
BL-LSTM
ST-LSTM
71
81
70
70
70
75 7.1%
71
79 10.9%
BL-Transformer
ST-Transformer
74
85
75
79
75
81 8.0%
74
84 12.5%
6.3. Downstream Task: Machine Translation
We measure the impact of segmentation and punctuation improvements on the downstream task
of MT. Higher quality punctuation leads to translation BLEU gains for all seven target languages,
as summarized in Table 6. The ST-Transformer system achieves the best results across all seven
target languages. On average, the ST-Transformer system has a BLEU gain of 0.66 over BL-
Transformer and wins for all target languages. Similarly, the ST-LSTM system has a BLEU gain
of 0.33 over BL-LSTM system and wins for five out of seven target languages. These results
support our hypothesis.
We used Azure Cognitive Services Translator API and compared them with reference
translations. For Portuguese (pt) and French (fr), ST-LSTM regresses slightly, while ST-
Transformer outperforms BL-Transformer. It is worth noting that ST-Transformer achieves
significant gains over BL-Transformer, +1.1 for German (de) and +1.4 for Greek (el). The results
suggest that punctuation has a higher impact on translation accuracy for some language pairs. For
some language pairs, translation is more robust to punctuation errors.
11. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
11
Table 6. Translation BLEU Results: English audio recognized, punctuated, and translated to seven
languages
Language Model BLEU Gain
de
BL-LSTM
ST-LSTM
36.0
36.6 +0.6
BL-Transformer
ST-Transformer
36.4
37.5 +1.1
el
BL-LSTM
ST-LSTM
39.8
40.8 +1.0
BL-Transformer
ST-Transformer
40.3
41.7 +1.4
fr
BL-LSTM
ST-LSTM
41.0
40.6 -0.4
BL-Transformer
ST-Transformer
41.7
41.8 +0.1
it
BL-LSTM
ST-LSTM
35.2
35.5 +0.3
BL-Transformer
ST-Transformer
35.4
35.9 +0.5
pl
BL-LSTM
ST-LSTM
30.2
30.9 +0.7
BL-Transformer
ST-Transformer
31.1
31.7 +0.6
pt
BL-LSTM
ST-LSTM
33.2
33 -0.2
BL-Transformer
ST-Transformer
33.7
33.9 +0.2
ro
BL-LSTM
ST-LSTM
39.8
40.1 +0.3
BL-Transformer
ST-Transformer
40.5
41.2 +0.7
Table 7. BL-Transformer’s incorrect punctuation leads to incorrect translations from English to select four
languages. ST-Transformer correctly punctuates, resulting in correct translations.
Language BL-Transformer ST-Transformer
en I. Just have to share the view . . . I just have to share the view . . .
de I. Ich muss nur die Ansicht teilen . . . Ich muss nur die Ansicht teilen . . .
fr I. Il suffit de partager le point de . . . Je dois simplement partager le point
de . . .
it I. Basti condividere l'opinione . . . Devo solo condividere l'opinione . . .
Table 7 presents an example of how incorrect punctuation can lead to downstream consequences
in machine translated outputs. Here BL-Transformer incorrectly punctuates after “I” which
results in (1) failure to accurately translate the word, (2) incorrect translations for the subsequent
text, and (3) incorrect punctuation in the translations to all languages. ST-Transformer, however,
correctly punctuates and thus produces correct translations. This example demonstrates the
importance of punctuation quality for downstream tasks such as MT.
12. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
12
7. CONCLUSION
Long pauses and hesitations occur naturally in dictation scenarios. We started this work to solve
the over-segmentation problem for long-form dictation users. We discovered these elements
affect other long-form transcription scenarios like conversations, meeting transcriptions, and
broadcasts. Our streaming punctuation approach improves punctuation for a variety of these ASR
scenarios. Higher quality punctuation directly leads to higher quality downstream tasks, such as
improvement in BLEU scores for machine translation applications. We also established the
efficacy of streaming punctuation across the transformer and LSTM tagging models, thus
establishing the robustness of streaming punctuation to different model architectures.
In this paper, we focused on improving punctuation for hybrid ASR systems. Our preliminary
analysis has found that though end-to-end (E2E) ASR systems produce better punctuation out of
the box, such systems have yet to fully solve the problem of over-segmentation and could benefit
from streaming re-punctuation techniques. We plan to present our findings in the future.
Streaming punctuation discussed here relies primarily on linguistic features and discards acoustic
signals. We plan to further extend this work using prosody-aware neural punctuation models. As
we explore streaming punctuation’s effectiveness and potential for other languages, we are also
interested in exploring the impact of intonation or accents on our method.
REFERENCES
[1] Elizabeth Shriberg, Andreas Stolcke, Dilek HakkaniTur, and Gokhan Tur, “Prosody-based automatic
segmentation of speech into sentences and topics,” Speech communication, vol. 32, no. 1-2, pp. 127–
154, 2000.
[2] Madina Hasan, Rama Doddipatla, and Thomas Hain, “Multi-pass sentence-end detection of lecture
speech,” in Fifteenth Annual Conference of the International Speech Communication Association,
2014.
[3] Piotr Zelasko, Piotr Szyma ˙ nski, Jan Mizgajski, Adrian Szymczak, Yishay Carmiel, and Najim
Dehak, “Punctuation prediction model for conversational speech,” arXiv preprint arXiv:1807.00543,
2018.
[4] Agustin Gravano, Martin Jansche, and Michiel Bacchiani, “Restoring punctuation and capitalization
in transcribed speech,” in 2009 IEEE International Conference on Acoustics, Speech, and Signal
Processing. IEEE, 2009, pp. 4741–4744.
[5] Wei Lu and Hwee Tou Ng, “Better punctuation prediction with dynamic conditional random fields,”
in Proceedings of the 2010 conference on empirical methods in natural language processing, 2010,
pp. 177–186.
[6] Xuancong Wang, Hwee Tou Ng, and Khe Chai Sim, “Dynamic conditional random fields for joint
sentence boundary and punctuation prediction,” in Thirteenth Annual Conference of the International
Speech Communication Association, 2012.
[7] Nicola Ueffing, Maximilian Bisani, and Paul Vozila, “Improved models for automatic punctuation
prediction for spoken and written text.,” in Interspeech, 2013, pp. 3097–3101.
[8] Jing Huang and Geoffrey Zweig, “Maximum entropy model for punctuation annotation from
speech.,” in Interspeech, 2002.
[9] Yang Liu, Elizabeth Shriberg, Andreas Stolcke, Dustin Hillard, Mari Ostendorf, and Mary Harper,
“Enriching speech recognition with automatic detection of sentence boundaries and disfluencies,”
IEEE Transactions on audio, speech, and language processing, vol. 14, no. 5, pp. 1526–1540, 2006.
[10] Ronan Collobert, Jason Weston, Leon Bottou, Michael, Karlen, Koray Kavukcuoglu, and Pavel
Kuksa, “Natural language processing (almost) from scratch,” Journal of machine learning research,
vol. 12, no. ARTICLE, pp. 2493–2537, 2011.
[11] Xiaoyin Che, Cheng Wang, Haojin Yang, and Christoph Meinel, “Punctuation prediction for
unsegmented transcript based on word vector,” in Proceedings of the Tenth International Conference
on Language Resources and Evaluation (LREC’16), 2016, pp. 654–658.
13. International Journal on Natural Language Computing (IJNLC) Vol.11, No.6, December 2022
13
[12] William Gale and Sarangarajan Parthasarathy, “Experiments in character-level neural network
models for punctuation.,” in INTERSPEECH, 2017, pp. 2794–2798.
[13] Vasile Pais¸ and Dan Tufis¸, “Capitalization and punctuation restoration: a survey,” Artificial
Intelligence Review, vol. 55, no. 3, pp. 1681–1722, 2022.
[14] Kaituo Xu, Lei Xie, and Kaisheng Yao, “Investigating lstm for punctuation prediction,” in 2016 10th
International Symposium on Chinese Spoken Language Processing (ISCSLP), 2016, pp. 1–5.
[15] Xuezhe Ma and Eduard Hovy, “End-to-end sequence labeling via bi-directional lstm-cnns-crf,” arXiv
preprint arXiv:1603.01354, 2016.
[16] Jiangyan Yi, Jianhua Tao, Zhengqi Wen, Ya Li, et al., “Distilling knowledge from an ensemble of
models for punctuation prediction.,” in Interspeech, 2017, pp. 2779–2783.
[17] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz
Kaiser, and Illia Polosukhin, “Attention is all you need,” Advances in neural information processing
systems, vol. 30, 2017.
[18] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, “Bert: Pre-training of deep
bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
[19] Jiangyan Yi, Jianhua Tao, Zhengkun Tian, Ye Bai, and Cunhang Fan, “Focal loss for punctuation
prediction.,” in INTERSPEECH, 2020, pp. 721–725.
[20] Yangjun Wu, Kebin Fang, and Yao Zhao, “A contextaware feature fusion framework for punctuation
restoration,” arXiv preprint arXiv:2203.12487, 2022.
[21] Maury Courtland, Adam Faulkner, and Gayle McElvain, “Efficient automatic punctuation restoration
using bidirectional transformers with robust inference,” in Proceedings of the 17th International
Conference on Spoken Language Translation, 2020, pp. 272–279.
[22] Tanvirul Alam, Akib Khan, and Firoj Alam, “Punctuation restoration using transformer models for
high-and low-resource languages,” in Proceedings of the Sixth Workshop on Noisy User-generated
Text (W-NUT 2020), 2020, pp. 132–142.
[23] Raghavendra Pappagari, Piotr Zelasko, Agnieszka Mikołajczyk, Piotr Pezik, and Najim Dehak, “Joint
prediction of truecasing and punctuation for conversational speech in low-resource scenarios,” in
2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2021, pp.
1185–1191.
[24] Attila Nagy, Bence Bial, and Judit Acs, “Automatic punctuation restoration with bert models,” arXiv
preprint arXiv:2101.07343, 2021.
[25] Aaron Gokaslan and Vanya Cohen, “Openwebtext corpus,”
http://Skylion007.github.io/OpenWebTextCorpus,2019.
[26] Pierre Lison and Jorg Tiedemann, “Opensubtitles2016: Extracting large parallel corpora from movie
and tv subtitles,” 2016.
[27] Jiazheng Li, Linyi Yang, Barry Smyth, and Ruihai Dong, “Maec: A multimodal aligned earnings
conference call dataset for financial risk prediction,” in Proceedings of the 29th ACM International
Conference on Information & Knowledge Management, 2020, pp. 3063–3070.
[28] Piyush Behre, Sharman Tan, Padma Varadharajan and Shuangyu Chang, “Streaming Punctuation for
Long-form Dictation with Transformers.” in Proceedings of the 8th
International Conference on
Signal, Image Processing and Embedded Systems, 2022, pp. 187-197. arXiv:2210.05756
[29] Elizabeth Shriberg, Andreas Stolcke, Dilek Hakkani-Tur, and Gokhan Tur, “Prosody-based automatic
segmentation of speech into sentences and topics,” Speech communication, vol. 32, no. 1-2, pp. 127–
154, 2000.
[30] Zulfiqar Ali and Muhammad Talha, “Innovative method for unsupervised voice activity detection and
classification of audio segments,” IEEE Access, vol. 6, pp. 15494– 15504, 2018.
[31] Junfeng Hou, Wu Guo, Yan Song, and Li-Rong Dai, “Segment boundary detection directed attention
for online end-to-end speech recognition,” EURASIP Journal on Audio, Speech, and Music
Processing, vol. 2020, no. 1, pp. 1–16, 2020.
[32] Piyush Behre, Naveen Parihar, Sharman Tan, Amy Shah, Eva Sharma, Geoffrey Liu, Shuangyu
Chang, Hosam Khalil, Chris Basoglu, and Sayan Pathak. “Smart Speech Segmentation using
Acousto-Linguistic Features with look-ahead,” arXiv preprint arXiv:2210.14446, 2022.
[33] W Ronny Huang, Shuo-yiin Chang, David Rybach, Rohit Prabhavalkar, Tara N Sainath, Cyril
Allauzen, Cal Peyser, and Zhiyun Lu, “E2E Segmenter: Joint segmenting and decoding for long-form
ASR,” arXiv preprint arXiv:2204.10749, 2022.
[34] “Debates and videos: Plenary: European parliament”
https://www.europarl.europa.eu/plenary/en/debates-video.html, Accessed: 2022-05-30.