In many situation such as TV narration & speech-based creativity, you may wanna control the prosody or pronunciation of synthetic speech. This method allows us to control synthetic speech using your voice.
The document describes the NAIST Text-to-Speech system developed for the Blizzard Challenge 2015. The system uses an HMM-based approach with 4 main modules: text processing, speech processing, training, and synthesis. New functions include parameter trajectory smoothing using modulation spectrum analysis in the speech processing module and incorporating modulation spectrum in the synthesis module. Evaluation results show the system ranked highly in naturalness and intelligibility for the Marathi language.
This document discusses approaches to improve the quality of statistical parametric speech synthesis. It proposes modeling individual speech segments using rich context Gaussian mixture models and integrating modulation spectrum constraints into the parameter generation process. Subjective evaluations found these approaches improved speech quality over hidden Markov model-based synthesis and Gaussian mixture model-based voice conversion.
APSIPA2017: Trajectory smoothing for vocoder-free speech synthesisShinnosuke Takamichi
This document discusses using modulation spectrum-based trajectory smoothing for DNN-based speech synthesis using FFT spectra. It proposes smoothing the trajectory of FFT spectral features by removing higher modulation frequency components that are difficult for statistical models to predict and negligible for speech perception. Experiments show this approach improves the training accuracy of acoustic models, as measured by lower mean squared error between natural and synthetic FFT spectra, without significantly degrading synthetic speech quality. The best results were obtained with a 30Hz low-pass filter cutoff modulation frequency.
This document provides an overview of BERT (Bidirectional Encoder Representations from Transformers) and how it works. It discusses BERT's architecture, which uses a Transformer encoder with no explicit decoder. BERT is pretrained using two tasks: masked language modeling and next sentence prediction. During fine-tuning, the pretrained BERT model is adapted to downstream NLP tasks through an additional output layer. The document outlines BERT's code implementation and provides examples of importing pretrained BERT models and fine-tuning them on various tasks.
Speaker recognition systems aim to automatically identify or verify a speaker's identity based on characteristics of their voice. There are two main types: speaker identification determines which registered speaker is speaking, while speaker verification accepts or rejects a speaker's claimed identity. All systems contain modules for feature extraction and feature matching. Feature extraction represents the voice signal with parameters like MFCCs that can distinguish speakers. Feature matching compares extracted features from an unknown voice to known speaker models. The document describes the process of MFCC feature extraction in detail, including framing the speech signal, windowing frames, taking the FFT, mapping to the mel scale, and finally the DCT to produce MFCC coefficients.
Approach To Build A Marathi Text-To-Speech System Using Concatenative Synthes...IJERA Editor
Marathi is one of the oldest languages in India. This research paper describes the development of Marathi Textto-
Speech System (TTS). In Marathi TTS the input is Marathi text in Unicode. The voices are sampled from real
recorded speech. The objective of a text to speech system is to convert an arbitrary text into its corresponding
spoken waveform. Speech synthesis is a process of building machinery that can generate human-like speech
from any text input to imitate human speakers. Text processing and speech generation are two main components
of a text to speech system. To build a natural sounding speech synthesis system, it is essential that text
processing component produce an appropriate sequence of phonemic units. Generation of sequence of phonetic
units for a given standard word is referred to as letter to phoneme rule or text to phoneme rule. The
complexity of these rules and their derivation depends upon the nature of the language. The quality of a speech
synthesizer is judged by its closeness to the natural human voice and understandability. In this research paper we
described an approach to build a Marathi TTS system using concatenative synthesis method with syllable as a
basic unit of concatenation.
AN ADVANCED APPROACH FOR RULE BASED ENGLISH TO BENGALI MACHINE TRANSLATIONcscpconf
This paper introduces an advanced, efficient approach for rule based English to Bengali (E2B) machine translation (MT), where Penn-Treebank parts of speech (PoS) tags, HMM (Hidden
Markov Model) Tagger is used. Fuzzy-If-Then-Rule approach is used to select the lemma from rule-based-knowledge. The proposed E2B-MT has been tested through F-Score measurement,
and the accuracy is more than eighty percent
BERT is a language representation model that was pre-trained using two unsupervised prediction tasks: masked language modeling and next sentence prediction. It uses a multi-layer bidirectional Transformer encoder based on the original Transformer architecture. BERT achieved state-of-the-art results on a wide range of natural language processing tasks including question answering and language inference. Extensive experiments showed that both pre-training tasks, as well as a large amount of pre-training data and steps, were important for BERT to achieve its strong performance.
The document describes the NAIST Text-to-Speech system developed for the Blizzard Challenge 2015. The system uses an HMM-based approach with 4 main modules: text processing, speech processing, training, and synthesis. New functions include parameter trajectory smoothing using modulation spectrum analysis in the speech processing module and incorporating modulation spectrum in the synthesis module. Evaluation results show the system ranked highly in naturalness and intelligibility for the Marathi language.
This document discusses approaches to improve the quality of statistical parametric speech synthesis. It proposes modeling individual speech segments using rich context Gaussian mixture models and integrating modulation spectrum constraints into the parameter generation process. Subjective evaluations found these approaches improved speech quality over hidden Markov model-based synthesis and Gaussian mixture model-based voice conversion.
APSIPA2017: Trajectory smoothing for vocoder-free speech synthesisShinnosuke Takamichi
This document discusses using modulation spectrum-based trajectory smoothing for DNN-based speech synthesis using FFT spectra. It proposes smoothing the trajectory of FFT spectral features by removing higher modulation frequency components that are difficult for statistical models to predict and negligible for speech perception. Experiments show this approach improves the training accuracy of acoustic models, as measured by lower mean squared error between natural and synthetic FFT spectra, without significantly degrading synthetic speech quality. The best results were obtained with a 30Hz low-pass filter cutoff modulation frequency.
This document provides an overview of BERT (Bidirectional Encoder Representations from Transformers) and how it works. It discusses BERT's architecture, which uses a Transformer encoder with no explicit decoder. BERT is pretrained using two tasks: masked language modeling and next sentence prediction. During fine-tuning, the pretrained BERT model is adapted to downstream NLP tasks through an additional output layer. The document outlines BERT's code implementation and provides examples of importing pretrained BERT models and fine-tuning them on various tasks.
Speaker recognition systems aim to automatically identify or verify a speaker's identity based on characteristics of their voice. There are two main types: speaker identification determines which registered speaker is speaking, while speaker verification accepts or rejects a speaker's claimed identity. All systems contain modules for feature extraction and feature matching. Feature extraction represents the voice signal with parameters like MFCCs that can distinguish speakers. Feature matching compares extracted features from an unknown voice to known speaker models. The document describes the process of MFCC feature extraction in detail, including framing the speech signal, windowing frames, taking the FFT, mapping to the mel scale, and finally the DCT to produce MFCC coefficients.
Approach To Build A Marathi Text-To-Speech System Using Concatenative Synthes...IJERA Editor
Marathi is one of the oldest languages in India. This research paper describes the development of Marathi Textto-
Speech System (TTS). In Marathi TTS the input is Marathi text in Unicode. The voices are sampled from real
recorded speech. The objective of a text to speech system is to convert an arbitrary text into its corresponding
spoken waveform. Speech synthesis is a process of building machinery that can generate human-like speech
from any text input to imitate human speakers. Text processing and speech generation are two main components
of a text to speech system. To build a natural sounding speech synthesis system, it is essential that text
processing component produce an appropriate sequence of phonemic units. Generation of sequence of phonetic
units for a given standard word is referred to as letter to phoneme rule or text to phoneme rule. The
complexity of these rules and their derivation depends upon the nature of the language. The quality of a speech
synthesizer is judged by its closeness to the natural human voice and understandability. In this research paper we
described an approach to build a Marathi TTS system using concatenative synthesis method with syllable as a
basic unit of concatenation.
AN ADVANCED APPROACH FOR RULE BASED ENGLISH TO BENGALI MACHINE TRANSLATIONcscpconf
This paper introduces an advanced, efficient approach for rule based English to Bengali (E2B) machine translation (MT), where Penn-Treebank parts of speech (PoS) tags, HMM (Hidden
Markov Model) Tagger is used. Fuzzy-If-Then-Rule approach is used to select the lemma from rule-based-knowledge. The proposed E2B-MT has been tested through F-Score measurement,
and the accuracy is more than eighty percent
BERT is a language representation model that was pre-trained using two unsupervised prediction tasks: masked language modeling and next sentence prediction. It uses a multi-layer bidirectional Transformer encoder based on the original Transformer architecture. BERT achieved state-of-the-art results on a wide range of natural language processing tasks including question answering and language inference. Extensive experiments showed that both pre-training tasks, as well as a large amount of pre-training data and steps, were important for BERT to achieve its strong performance.
Limited Data Speaker Verification: Fusion of FeaturesIJECEIAES
The present work demonstrates experimental evaluation of speaker verification for dif- ferent speech feature extraction techniques with the constraints of limited data (less than 15 seconds). The state-of-the-art speaker verification techniques provide good performance for sufficient data (greater than 1 minutes). It is a challenging task to develop techniques which perform well for speaker verification under limited data condition. In this work different features like Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficients (LPCC), Delta (4), Delta-Delta (44), Linear Prediction Residual (LPR) and Linear Prediction Residual Phase (LPRP) are considered. The performance of individual features is studied and for better verification performance, combination of these features is attempted. A comparative study is made between Gaussian mixture model (GMM) and GMM-universal background model (GMM-UBM) through experimental evaluation. The experiments are conducted using NIST-2003 database. The experimental results show that, the combination of features provides better performance compared to the individual features. Further GMM-UBM modeling gives reduced equal error rate (EER) as compared to GMM.
A Marathi Hidden-Markov Model Based Speech Synthesis Systemiosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
BERT was developed by Google AI Language and came out Oct. 2018. It has achieved the best performance in many NLP tasks. So if you are interested in NLP, studying BERT is a good way to go.
This document provides an overview of hidden Markov models (HMMs) and their application in large vocabulary continuous speech recognition (LVCSR) systems. It describes the basic architecture of an HMM-based speech recognizer, including components like feature extraction, acoustic models, a pronunciation dictionary, language model, and decoder. It then discusses various refinements that are needed to achieve state-of-the-art performance, such as feature transformations, more complex HMM output distributions, discriminative training methods, adaptation techniques, and multi-pass recognition architectures.
The document proposes a new optimization algorithm called the Generalized Baum-Welch (GBW) algorithm for discriminative training on hidden Markov models. GBW is based on Lagrange relaxation of a transformed optimization problem. The Baum-Welch algorithm for maximum likelihood estimation of HMM parameters and the extended Baum-Welch algorithm for discriminative training are both special cases of GBW. The performance of GBW and EBW are compared for a Farsi large vocabulary continuous speech recognition task.
Paper Introduction,
"Translating into Morphologically Rich Languages with Synthetic Phrases"
Victor Chahuneau, Eva Schlinger, Noah A. Smith, Chris Dyer (EMNLP2013)
The first FOSD-tacotron-2-based text-to-speech application for VietnamesejournalBEEI
Recently, with the development and deployment of voicebots which help to minimize personnels at call centers, text-to-speech (TTS) systems supporting English and Chinese have attracted attentions of researchers and corporates worldwide. However, there is very limited published works in TTS developed for Vietnamese. Thus, this paper presents in detail the first Tacotron-2-based TTS application development for Vietnamese that utilizes the publicly available FPT open speech dataset (FOSD) containing approximately 30 hours of labeled audio files together with their transcripts. The dataset was made available by FPT Corporation with an open access license. A new cleaner was developed for supporting Vietnamese language rather than English which was provided by default in Mozilla TTS source code. After 225,000 training steps, the generated speeches have mean opinion score (MOS) well above the average value of 2.50 and center around 3.00 for both clearness and naturalness in a crowd-source survey.
This paper proposes a voice morphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A primitive method of achieving voice morphing is by extracting the source's vocal coefficients and then converting them into the target speaker's vocal parameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mapping the coefficients from source to destination. However, the use of the traditional/conventional GMM-based mapping approach results in the problem of over-smoothening of the converted voice. Thus, we hereby propose a unique method to perform efficient voice morphing and conversion based on GMM, which overcomes the traditional-method effects of over-smoothening. It uses a technique of glottal waveform separation and prediction of excitations and hence the result shows that not only over-smoothening is eliminated but also the transformed vocal tract parameters match with the target. Moreover, the synthesized speech thus obtained is found to be of a sufficiently high quality. Thus, voice morphing based on a unique GMM approach has been proposed and also critically evaluated based on various subjective and objective evaluation parameters. Further, an application of voice morphing for Laryngectomees which deploys this unique approach has been recommended by this paper
Voice morphing is a technique that modifies a source speaker's speech to sound like it was spoken by a target speaker. It works by analyzing the source speech into an excitation signal and filter components, then resynthesizing it with the pitch and vocal characteristics of the target speaker. The key steps are detecting the pitches of the source and target speakers, scaling the source pitch to match the target, then resynthesizing the source speech using the target's vocal filter characteristics and the pitch-scaled excitation signal. Voice morphing was developed in 1999 and has applications in text-to-speech, dubbing, voice disguising, and public announcement systems.
This preseantation addresses the impact of multiword translation errors in machine translation (MT). We have analysed translations of multiwords in the OpenLogos
rule-based system (RBMT) and in the Google Translate statistical system (SMT) for the English-French, English-Italian, and English-Portuguese language pairs. Our study shows that, for distinct reasons, multiwords remain a problematic area for MT independently of the approach, and require adequate linguistic quality evaluation metrics founded on a systematic categorization of errors by MT expert linguists. We propose an empirically-driven taxonomy for multiwords, and highlight the need for the development of specific corpora for multiword evaluation. Finally, the paper presents the Logos approach to multiword processing, illustrating how semantico-syntactic rules contribute to multiword translation quality.
This document discusses speech user interfaces (SUI), which allow users to control computers using voice commands. It outlines the need for SUI to provide hands-free access for various users, including opportunities for illiterate populations. The document then covers an overview of speech recognition techniques like MFCC and HMM for feature extraction. It describes implementing an SUI, including recording speech, training models, and recognizing commands. Example applications are voice dialing, assistants, and accessibility tools. The document concludes by noting future areas like language learning and medical dictation, as well as challenges like vocabulary size and noise interference.
This document discusses various methods for analyzing speech signals using Matlab, including fundamental frequency estimation in both the frequency and time domains, and formant frequency estimation using linear predictive coding. Code examples are provided for estimating fundamental frequency from the peak in a signal's cepstrum and autocorrelation function, and for using LPC to find the best IIR filter for a speech segment and plot the filter's frequency response to estimate formant frequencies.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
1) The document proposes a training algorithm to deceive anti-spoofing verification for DNN-based speech synthesis. It trains acoustic models through an iterative process of updating the models and anti-spoofing discriminator.
2) The algorithm aims to improve speech quality by compensating for differences between natural and generated speech parameter distributions using adversarial training.
3) Evaluation results show the algorithm improves speech quality over conventional training, while also training the models to effectively deceive the anti-spoofing system. The quality gains are robust against hyperparameter settings.
Limited Data Speaker Verification: Fusion of FeaturesIJECEIAES
The present work demonstrates experimental evaluation of speaker verification for dif- ferent speech feature extraction techniques with the constraints of limited data (less than 15 seconds). The state-of-the-art speaker verification techniques provide good performance for sufficient data (greater than 1 minutes). It is a challenging task to develop techniques which perform well for speaker verification under limited data condition. In this work different features like Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficients (LPCC), Delta (4), Delta-Delta (44), Linear Prediction Residual (LPR) and Linear Prediction Residual Phase (LPRP) are considered. The performance of individual features is studied and for better verification performance, combination of these features is attempted. A comparative study is made between Gaussian mixture model (GMM) and GMM-universal background model (GMM-UBM) through experimental evaluation. The experiments are conducted using NIST-2003 database. The experimental results show that, the combination of features provides better performance compared to the individual features. Further GMM-UBM modeling gives reduced equal error rate (EER) as compared to GMM.
A Marathi Hidden-Markov Model Based Speech Synthesis Systemiosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
BERT was developed by Google AI Language and came out Oct. 2018. It has achieved the best performance in many NLP tasks. So if you are interested in NLP, studying BERT is a good way to go.
This document provides an overview of hidden Markov models (HMMs) and their application in large vocabulary continuous speech recognition (LVCSR) systems. It describes the basic architecture of an HMM-based speech recognizer, including components like feature extraction, acoustic models, a pronunciation dictionary, language model, and decoder. It then discusses various refinements that are needed to achieve state-of-the-art performance, such as feature transformations, more complex HMM output distributions, discriminative training methods, adaptation techniques, and multi-pass recognition architectures.
The document proposes a new optimization algorithm called the Generalized Baum-Welch (GBW) algorithm for discriminative training on hidden Markov models. GBW is based on Lagrange relaxation of a transformed optimization problem. The Baum-Welch algorithm for maximum likelihood estimation of HMM parameters and the extended Baum-Welch algorithm for discriminative training are both special cases of GBW. The performance of GBW and EBW are compared for a Farsi large vocabulary continuous speech recognition task.
Paper Introduction,
"Translating into Morphologically Rich Languages with Synthetic Phrases"
Victor Chahuneau, Eva Schlinger, Noah A. Smith, Chris Dyer (EMNLP2013)
The first FOSD-tacotron-2-based text-to-speech application for VietnamesejournalBEEI
Recently, with the development and deployment of voicebots which help to minimize personnels at call centers, text-to-speech (TTS) systems supporting English and Chinese have attracted attentions of researchers and corporates worldwide. However, there is very limited published works in TTS developed for Vietnamese. Thus, this paper presents in detail the first Tacotron-2-based TTS application development for Vietnamese that utilizes the publicly available FPT open speech dataset (FOSD) containing approximately 30 hours of labeled audio files together with their transcripts. The dataset was made available by FPT Corporation with an open access license. A new cleaner was developed for supporting Vietnamese language rather than English which was provided by default in Mozilla TTS source code. After 225,000 training steps, the generated speeches have mean opinion score (MOS) well above the average value of 2.50 and center around 3.00 for both clearness and naturalness in a crowd-source survey.
This paper proposes a voice morphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A primitive method of achieving voice morphing is by extracting the source's vocal coefficients and then converting them into the target speaker's vocal parameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mapping the coefficients from source to destination. However, the use of the traditional/conventional GMM-based mapping approach results in the problem of over-smoothening of the converted voice. Thus, we hereby propose a unique method to perform efficient voice morphing and conversion based on GMM, which overcomes the traditional-method effects of over-smoothening. It uses a technique of glottal waveform separation and prediction of excitations and hence the result shows that not only over-smoothening is eliminated but also the transformed vocal tract parameters match with the target. Moreover, the synthesized speech thus obtained is found to be of a sufficiently high quality. Thus, voice morphing based on a unique GMM approach has been proposed and also critically evaluated based on various subjective and objective evaluation parameters. Further, an application of voice morphing for Laryngectomees which deploys this unique approach has been recommended by this paper
Voice morphing is a technique that modifies a source speaker's speech to sound like it was spoken by a target speaker. It works by analyzing the source speech into an excitation signal and filter components, then resynthesizing it with the pitch and vocal characteristics of the target speaker. The key steps are detecting the pitches of the source and target speakers, scaling the source pitch to match the target, then resynthesizing the source speech using the target's vocal filter characteristics and the pitch-scaled excitation signal. Voice morphing was developed in 1999 and has applications in text-to-speech, dubbing, voice disguising, and public announcement systems.
This preseantation addresses the impact of multiword translation errors in machine translation (MT). We have analysed translations of multiwords in the OpenLogos
rule-based system (RBMT) and in the Google Translate statistical system (SMT) for the English-French, English-Italian, and English-Portuguese language pairs. Our study shows that, for distinct reasons, multiwords remain a problematic area for MT independently of the approach, and require adequate linguistic quality evaluation metrics founded on a systematic categorization of errors by MT expert linguists. We propose an empirically-driven taxonomy for multiwords, and highlight the need for the development of specific corpora for multiword evaluation. Finally, the paper presents the Logos approach to multiword processing, illustrating how semantico-syntactic rules contribute to multiword translation quality.
This document discusses speech user interfaces (SUI), which allow users to control computers using voice commands. It outlines the need for SUI to provide hands-free access for various users, including opportunities for illiterate populations. The document then covers an overview of speech recognition techniques like MFCC and HMM for feature extraction. It describes implementing an SUI, including recording speech, training models, and recognizing commands. Example applications are voice dialing, assistants, and accessibility tools. The document concludes by noting future areas like language learning and medical dictation, as well as challenges like vocabulary size and noise interference.
This document discusses various methods for analyzing speech signals using Matlab, including fundamental frequency estimation in both the frequency and time domains, and formant frequency estimation using linear predictive coding. Code examples are provided for estimating fundamental frequency from the peak in a signal's cepstrum and autocorrelation function, and for using LPC to find the best IIR filter for a speech segment and plot the filter's frequency response to estimate formant frequencies.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
1) The document proposes a training algorithm to deceive anti-spoofing verification for DNN-based speech synthesis. It trains acoustic models through an iterative process of updating the models and anti-spoofing discriminator.
2) The algorithm aims to improve speech quality by compensating for differences between natural and generated speech parameter distributions using adversarial training.
3) Evaluation results show the algorithm improves speech quality over conventional training, while also training the models to effectively deceive the anti-spoofing system. The quality gains are robust against hyperparameter settings.
2017年6月24日,ICASSP2017読み会(関東編)@東京大学
AASP-L3: Deep Learning for Source Separation and Enhancement I
東京大学特任助教 北村大地担当分のスライド
私が著者ではないペーパーの紹介スライドですので,再配布等はご遠慮ください.また,このスライドで取り扱っていない詳細な情報に関しては対象となる論文をご参照ください.
Evaluation of Hidden Markov Model based Marathi Text-ToSpeech Synthesis SystemIJERA Editor
The objective of this paper is to evaluate the quality of HMM based Marathi TTS system. The main advantage of HMM technique is its ability to allow the variation in voice easily. The output speeches produced in this method have greater impact on emotion, style and intonation. The naturalness and intelligibility are the two important parameters to decide the quality of synthetic speech. Depending on the parameters specified the results of synthetic speech are categorized into 4 categories: natural speech, high quality synthetic speech, low quality synthetic speech and moderate quality synthetic speech. The results are obtained by using CT, DRT and MOS test.
It is a technique to modify a source speaker's speech to sound as if it was spoken by a target speaker.
Voice morphing enables speech patterns to be cloned
And an accurate copy of a person's voice can be made that can wishes to say, anything in the voice of someone else.
Voice morphing is a technique that modifies a source speaker's speech to sound like it was spoken by a target speaker. It works by extracting the pitch and formant information from both voices and using dynamic time warping to align the pitches. The frames are then converted back to a waveform to create a synthesized voice with the target speaker's characteristics. Potential applications include text-to-speech systems, special effects, and diminishing ethnic barriers, though it has limitations from normalization problems and requires extensive sound libraries. The future scope is to create a more powerful and flexible morphing tool with increased user interaction.
What can GAN and GMMN do for augmented speech communication? Shinnosuke Takamichi
1) The document discusses how generative adversarial networks (GANs) and generative moment matching networks (GMMNs) can improve augmented speech communication.
2) GANs and GMMNs have been used to improve the quality of text-to-speech synthesis and allow for random sampling of speech while preserving quality.
3) GMMNs are particularly useful for generating random speech due to their ability to explicitly model moments in speech and their easier optimization compared to GANs.
Speech recognition technology allows users to communicate through spoken commands. It works by converting acoustic speech signals captured by a microphone into text. There are two main types of speech models - speaker independent models that can recognize many people, and speaker dependent models customized for a single person. The speech recognition process involves an audio input being digitized, then broken down into phonemes which are statistically modeled and matched to words in a grammar according to a dictionary to output recognized text.
Behzad Ghorbani presented research on unsupervised cross-lingual speaker adaptation for text-to-speech synthesis. The goal was to personalize speech-to-speech translation by adapting synthesized speech output to the user's voice using speech recognition. Three studies on unsupervised and cross-lingual adaptation approaches were discussed: 1) Finnish-English using decision tree construction, 2) Chinese-English comparing supervised and unsupervised schemes, and 3) English-Japanese using unsupervised adaptation and evaluation of synthetic speech quality.
Performance Calculation of Speech Synthesis Methods for Hindi languageiosrjce
The document compares the performance of two speech synthesis methods - unit selection and hidden Markov model (HMM) - for Hindi language. It finds that unit selection results in higher quality synthesized speech than HMM based on both subjective and objective quality measurements. Subjective measurements using mean opinion scores show unit selection receives higher average ratings. Objective measurements of mean square error and peak signal-to-noise ratio also indicate unit selection introduces less distortion compared to the original speech samples.
The primary goal of this paper is to provide an overview of existing Text-To-Speech (TTS) Techniques by highlighting its usage and advantage. First Generation Techniques includes Formant Synthesis and Articulatory Synthesis. Formant Synthesis works by using individually controllable formant filters, which can be set to produce accurate estimations of the vocal-track transfer function. Articulatory Synthesis produces speech by direct modeling of Human articulator behavior. Second Generation Techniques incorporates Concatenative synthesis and Sinusoidal synthesis. Concatenative synthesis generates speech output by concatenating the segments of recorded speech. Generally, Concatenative synthesis generates the natural sounding synthesized speech. Sinusoidal Synthesis use a harmonic model and decompose each frame into a set of harmonics of an estimated fundamental frequency. The model parameters are the amplitudes and periods of the harmonics. With these, the value of the fundamental can be changed while keeping the same basic spectral..In adding, Third Generation includes Hidden Markov Model (HMM) and Unit Selection Synthesis.HMM trains the parameter module and produce high quality Speech. Finally, Unit Selection operates by selecting the best sequence of units from a large speech database which matches the specification.
This document describes a factored statistical machine translation system from English to Tamil that incorporates Tamil morphology. The system first reorders and factors the English text, then uses morphological analysis and generation tools for Tamil to further factorize the text. This addresses challenges of translating between languages with different morphological structures and word orders. The system was shown to improve over a baseline SMT system for English to Tamil translation by integrating linguistic information like lemmas and morphological features.
This document discusses homomorphic speech processing and techniques for speech enhancement. It provides an overview of modeling speech production as the excitation of a linear time-invariant system. Homomorphic filtering is introduced as a way to deconvolve speech into excitation and system response using logarithmic transformations. The complex cepstrum is discussed as a representation of speech that can be used to estimate pitch, voicing and formant frequencies. Homomorphic vocoding is described as a speech coding technique that quantizes the low-time part of the cepstrum at regular intervals to encode speech. Common techniques for speech enhancement like spectral subtraction and adaptive noise cancellation are also mentioned.
Hindi digits recognition system on speech data collected in different natural...csandit
This paper presents a baseline digits speech recognizer for Hindi language. The recording environment is different for all speakers, since the data is collected in their respective homes. The different environment refers to vehicle horn noises in some road facing rooms, internal background noises in some rooms like opening doors, silence in some rooms etc. All these recordings are used for training acoustic model. The Acoustic Model is trained on 8 speakers’ audio data. The vocabulary size of the recognizer is 10 words. HTK toolkit is used for building
acoustic model and evaluating the recognition rate of the recognizer. The efficiency of the recognizer developed on recorded data, is shown at the end of the paper and possible directions for future research work are suggested.
This document provides an overview of speech recognition including:
- The topics that will be covered such as speech production, why speech recognition is difficult, and applications.
- How speech is produced through the lungs, larynx, and vocal tract and modified into different sounds.
- The main components of a speech recognition system including sound sampling, conversion to frequencies, and matching to a phoneme database.
- Some of the challenges in speech recognition including variations between speakers and dependence on neighboring sounds.
Effect of MFCC Based Features for Speech Signal Alignmentskevig
The fundamental techniques used for man-machine communication include Speech synthesis, speech
recognition, and speech transformation. Feature extraction techniques provide a compressed
representation of the speech signals. The HNM analyses and synthesis provides high quality speech with
less number of parameters. Dynamic time warping is well known technique used for aligning two given
multidimensional sequences. It locates an optimal match between the given sequences. The improvement in
the alignment is estimated from the corresponding distances. The objective of this research is to investigate
the effect of dynamic time warping on phrases, words, and phonemes based alignments. The speech signals
in the form of twenty five phrases were recorded. The recorded material was segmented manually and
aligned at sentence, word, and phoneme level. The Mahalanobis distance (MD) was computed between the
aligned frames. The investigation has shown better alignment in case of HNM parametric domain. It has
been seen that effective speech alignment can be carried out even at phrase level
EFFECT OF MFCC BASED FEATURES FOR SPEECH SIGNAL ALIGNMENTSijnlc
The fundamental techniques used for man-machine communication include Speech synthesis, speech
recognition, and speech transformation. Feature extraction techniques provide a compressed
representation of the speech signals. The HNM analyses and synthesis provides high quality speech with
less number of parameters. Dynamic time warping is well known technique used for aligning two given
multidimensional sequences. It locates an optimal match between the given sequences. The improvement in
the alignment is estimated from the corresponding distances. The objective of this research is to investigate
the effect of dynamic time warping on phrases, words, and phonemes based alignments. The speech signals
in the form of twenty five phrases were recorded. The recorded material was segmented manually and
aligned at sentence, word, and phoneme level. The Mahalanobis distance (MD) was computed between the
aligned frames. The investigation has shown better alignment in case of HNM parametric domain. It has
been seen that effective speech alignment can be carried out even at phrase level.
Effect of Dynamic Time Warping on Alignment of Phrases and Phonemeskevig
Speech synthesis and recognition are the basic techniques used for man-machine communication. This type
of communication is valuable when our hands and eyes are busy in some other task such as driving a
vehicle, performing surgery, or firing weapons at the enemy. Dynamic time warping (DTW) is mostly used
for aligning two given multidimensional sequences. It finds an optimal match between the given sequences.
The distance between the aligned sequences should be relatively lesser as compared to unaligned
sequences. The improvement in the alignment may be estimated from the corresponding distances. This
technique has applications in speech recognition, speech synthesis, and speaker transformation. The
objective of this research is to investigate the amount of improvement in the alignment corresponding to the
sentence based and phoneme based manually aligned phrases. The speech signals in the form of twenty five
phrases were recorded from each of six speakers (3 males and 3 females). The recorded material was
segmented manually and aligned at sentence and phoneme level. The aligned sentences of different speaker
pairs were analyzed using HNM and the HNM parameters were further aligned at frame level using DTW.
Mahalanobis distances were computed for each pair of sentences. The investigations have shown more than
20 % reduction in the average Mahalanobis distances.
EFFECT OF DYNAMIC TIME WARPING ON ALIGNMENT OF PHRASES AND PHONEMESkevig
Speech synthesis and recognition are the basic techniques used for man-machine communication. This type
of communication is valuable when our hands and eyes are busy in some other task such as driving a
vehicle, performing surgery, or firing weapons at the enemy. Dynamic time warping (DTW) is mostly used
for aligning two given multidimensional sequences. It finds an optimal match between the given sequences.
The distance between the aligned sequences should be relatively lesser as compared to unaligned
sequences. The improvement in the alignment may be estimated from the corresponding distances. This
technique has applications in speech recognition, speech synthesis, and speaker transformation. The
objective of this research is to investigate the amount of improvement in the alignment corresponding to the
sentence based and phoneme based manually aligned phrases. The speech signals in the form of twenty five
phrases were recorded from each of six speakers (3 males and 3 females). The recorded material was
segmented manually and aligned at sentence and phoneme level. The aligned sentences of different speaker
pairs were analyzed using HNM and the HNM parameters were further aligned at frame level using DTW.
Mahalanobis distances were computed for each pair of sentences. The investigations have shown more than
20 % reduction in the average Mahalanobis distances.
This document presents an overview of voice morphing technology. It discusses that voice morphing is a technique to modify a source speaker's voice to sound like a target speaker. It describes the need for voice morphing in applications like text-to-speech, public address systems, and for special effects. The technical process involves extracting spectral and pitch information from both voices and using algorithms like dynamic time warping and signal re-estimation to morph the source voice into the target voice. Some applications discussed are for altering evidence in courts or creating fake orders in military conflicts.
IRJET- Designing and Creating Punjabi Speech Synthesis System using Hidden Ma...IRJET Journal
This document describes the design of a Punjabi speech synthesis system using Hidden Markov Models. It discusses collecting Punjabi text from various domains to build a speech corpus. Features are extracted from the text and stored in a database. The system has offline and online phases, where the database is created offline and text-to-speech conversion occurs online. Hidden Markov Models are used for statistical parametric speech synthesis, modeling acoustic features like fundamental frequency, duration, and spectrum. The system breaks text into phonetic units like phonemes and diphones to generate waveforms for natural-sounding synthesized speech.
This paper describe the morphing concept in which we convert the voice of any person into pre -analyzed or pre-recorded voice of any animals.As the user generate a pre-established voice, his pitch, timbre, vibrato and articulation can be modified to resemble those of a pre-recorded and pre-analyzed voice of animal. This technique is based on SMS. Thus using this concept we can develop many funny application and we can used this type of application in mobile device, personal computer etc. for enjoying the sometime of period.
Voice morphing is a technique that modifies a source speaker's speech to sound like it was spoken by a different target speaker. The process involves preprocessing the speech signal, analyzing the pitch and envelope, morphing through warping and interpolation, and re-estimating the signal. To morph voices between a male and female speaker, the pitch of the male speaker is shifted to match that of the female speaker by time-stretching the residue signal and adjusting the LPC coefficients. Potential applications include using popular speakers for public announcements, and effects in films, but limitations include difficulties in voice detection and updating systems for new languages.
Similar to Prosody-Controllable HMM-Based Speech Synthesis Using Speech Input (20)
The document describes a real-time DNN voice conversion system with feedback to acquire character traits. It proposes a method to provide real-time feedback of the converted voice to the speaker to encourage speech modification (prosody and emphasis) towards the target speaker's character. Subjective evaluations from the first-person (user) perspective and third-person perspective found that the system improved the reproduction of the target speaker's character, especially for inexperienced users. Providing only pitch feedback was already quite effective.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
2. /17
Speech-based creative activities
and HMM-based speech synthesis
2
Singing voice Speech
Advertisement Live concert Narration Next?
Video avatar
Voice actor
…
Useful method: HMM-based speech synthesis [Tokuda et al., 2013.]
“Synthesize!”
Synthetic speech parameters
text speech
3. /17
Manual control of synthetic speech
Laugh
Sad
Regression
Multi-Regression HMM [Nose et al., 2007.]
Manually manipulating HMM parameters
User
User
They are very useful, but difficult to control as the user wants.
4. /17
Motivation of this study
Functions we want
– Original capability of HMM-based TTS
– Speech-based control
• Intuitive to control
• Make synthetic speech mimic input speech prosody
Our work
– Speech synthesis having both functions
4
Synthesize
System
Synthesize“Synthesize.”
MR-HMM etc.
Similar to VOCALISTENER
for singing voice control
5. /17
Overview of the proposed system
(Only text is input.)
5
Input text
Text analysis
Waveform generation
Synthetic speech
Parameter
generation
Synthesis
HMM
Original HMM-based
speech synthesis
6. /17
Overview of the proposed system
(Text & speech are input.)
6
Input textInput speech
Speech analysis Text analysis
Waveform generation
Synthetic speech
F0
modification
Duration
extraction
Parameter
generation
Alignment
HMM
Synthesis
HMM
8. /17
Alignment accuracy & duration unit
How to build alignment HMMs suitable for input speech?
– → The use of pre-recorded speech uttered by users
– Large amounts → user-dependent HMMs
– Small amounts → HMMs adapted from original alignment HMMs
How to map the input speech duration to synthetic speech?
– Alignment/synthesis HMM-states represent different speech segments.
– Which is better, HMM-state, phone, or mora-level duration unit?
8
9. /17
Speech parameter generation module
9
Synthesis
HMM
Context of
Input text
Parameter
generation
Spectrum of
synthetic speech
F0 generated
From HMMs
Dur. ext.
State duration
F0 mod. Wav. Gen.
10. /17
F0 modification module
10
Feature of
input speech
F0 generated
from HMMs
F0
conversion
U/V region
modification
Parm. gen.
F0 of
synthetic speech
Wav. Gen.
11. /17
F0 conversion &
unvoiced/voiced modification
11
F0
Time
Reference
generated from HMMs
Input speech
F0-converted
U/V-modified
F0 conversion fixes F0 range of input speech to fit to reference.
U/V modification fixes the U/V region of input speech to fit to reference.
Linear
conversion
Spline
interpolation
13. /17
Experimental Setup
13
Content Value/Setting
User 4 Japanese speakers (2 male & 2 female)
Target speaker 1 Japanese female speaker
Training data of
synthesis HMMs
450 phoneme-balanced sentences,
16 kHz-sampled, 5 ms shift, reading style
Evaluation data 53 phoneme-balanced sentences
Speech features 25-dim. mel-cestrum, log F0, 5-band aperiodicity
Speech analyzer STRAIGHT [Kawahara et al., 1999.]
Text analyzer Open-jtalk
Acoustic model 5-state HSMM [Zen et al., 2007.]
1. duration unit & alignment HMM adaptation
2. synthesis HMM adaptation
3. effect of U/V modification
14. /17
Evaluation 1: duration unit &
alignment HMM adaptation
3 duration units
– State / phoneme / mora-level duration
4 HMMs using different amounts of pre-recorded speech
– 0 … target-speaker-dependent HMMs (= synthesis HMM)
– 1 … HMMs adapted using 1 utterance uttered by the user
– 56 … HMMs adapted using 56 utterances
– 450 … user-dependent HMMs
Evaluation
– MOS test on naturalness of synthetic speech
– DMOS test on prosody mimicking ability of synthetic speech
• Input speech is presented as reference.
14
15. /17
Result 1: duration unit &
alignment HMM adaptation
15
1
2
3
4
5
MOS on naturalness DMOS on prosody mimicking ability
0 1 56 450utts.
We can confirm (1) adaptation is effective, and
(2) phoneme-level dur. is relatively robust.
No significant diff. No significant diff.
state phone mora
16. /17
Experiment 2: Effectiveness of U/V
modification in naturalness
Preferencescoreonnaturalness[%]
0
20
40
60
80
100
Spkr1 Spkr2 Spkr3 Spkr4
U/Vmodificationratio[%]
0
5
10
15
20
Spkr1 Spkr2 Spkr3 Spkr4
w/o or w/ modification U->V or V->U modification
U/V modification can improve the naturalness!
(especially when many U frames of input speech are fixed.)
17. /17
Conclusion
2 functions to control synthetic speech
– An original function of HMM-based TTS
• MR-HMM or manual control
– Speech-based control
• Intuitive for users
2 main modules of our system
– Mimic duration.
• Copy duration of input speech to synthetic speech.
– Mimic F0 patterns.
• Copy dynamic F0 pattern of input speech to synthetic speech.
Future work
– HMM selection using text & speech 17