This is the presentation of our IEEE ICASSP 2021 paper "seen and unseen emotional style transfer for voice conversion with a new emotional speech dataset".
This presentation discusses designing an English language compiler to detect emotion from text. It begins with an introduction to emotion and common emotion models. It then outlines the objectives and architecture of the emotion detection system. Key aspects covered include language processing techniques like keyword analysis and parsing, semantic analysis, and the word-processing and sentence analysis modules. Challenges in developing such a system are also discussed. Finally, potential future work and references are presented.
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.
In this presentation, two different data-sets are being collected to implement the machine learning classification techniques introduced from introduction to data mining and machine learning coursework. Both data-sets are collected by analyzing their output and team members interest. Following are the data-sets named as, Electricity grid stability simulated data-set and Face Recognition on Olivetti Data set
The document discusses speech emotion recognition using machine learning. It aims to build a model to recognize emotion from speech using the librosa and sklearn libraries and the RAVDESS dataset. It extracts MFCC, mel spectrogram, and chroma features from the dataset and uses an MLP classifier to classify emotions into 8 categories with an accuracy of 66.67%. The model works best at identifying calm emotions and gets confused between similar emotions. Future work could explore using larger datasets with CNN, RNN models on different speakers and accents.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
This presentation discusses designing an English language compiler to detect emotion from text. It begins with an introduction to emotion and common emotion models. It then outlines the objectives and architecture of the emotion detection system. Key aspects covered include language processing techniques like keyword analysis and parsing, semantic analysis, and the word-processing and sentence analysis modules. Challenges in developing such a system are also discussed. Finally, potential future work and references are presented.
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.
In this presentation, two different data-sets are being collected to implement the machine learning classification techniques introduced from introduction to data mining and machine learning coursework. Both data-sets are collected by analyzing their output and team members interest. Following are the data-sets named as, Electricity grid stability simulated data-set and Face Recognition on Olivetti Data set
The document discusses speech emotion recognition using machine learning. It aims to build a model to recognize emotion from speech using the librosa and sklearn libraries and the RAVDESS dataset. It extracts MFCC, mel spectrogram, and chroma features from the dataset and uses an MLP classifier to classify emotions into 8 categories with an accuracy of 66.67%. The model works best at identifying calm emotions and gets confused between similar emotions. Future work could explore using larger datasets with CNN, RNN models on different speakers and accents.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
This document discusses sentiment analysis on Twitter data using machine learning classifiers. It describes Twitter sentiment analysis as determining if a tweet is positive, negative, or neutral. Some challenges are that people express opinions complexly using sarcasm, irony, and slang. The document tests different classifiers like Naive Bayes and SVM on Twitter data preprocessed by tokenizing, extracting sentiment features, and part-of-speech tagging. It finds that extracting more features like sentiment and part-of-speech tags along with an SVM classifier achieves the best accuracy of 68% at determining tweet sentiment.
This document discusses methods for evaluating language models, including intrinsic and extrinsic evaluation. Intrinsic evaluation involves measuring a model's performance on a test set using metrics like perplexity, which is based on how well the model predicts the test set. Extrinsic evaluation embeds the model in an application and measures the application's performance. The document also covers techniques for dealing with unknown words like replacing low-frequency words with <UNK> and estimating its probability from training data.
Brief introduction on attention mechanism and its application in neural machine translation, especially in transformer, where attention was used to remove RNNs completely from NMT.
Speech recognition systems convert spoken words to text in real-time. They are used in dictation software and intelligent assistants. Design challenges include background noise, accent variations, and speed of speech. Speaker dependent systems recognize one voice, while speaker independent systems recognize any voice without training. Speech is broken into phonemes and a hidden Markov model identifies phonemes and language models recognize words. Components include signal analysis, acoustic and language models. Applications include healthcare, military, phones, and personal computers. Siri and Google Now are examples of intelligent assistants using these techniques.
Residual neural networks (ResNets) solve the vanishing gradient problem through shortcut connections that allow gradients to flow directly through the network. The ResNet architecture consists of repeating blocks with convolutional layers and shortcut connections. These connections perform identity mappings and add the outputs of the convolutional layers to the shortcut connection. This helps networks converge earlier and increases accuracy. Variants include basic blocks with two convolutional layers and bottleneck blocks with three layers. Parameters like number of layers affect ResNet performance, with deeper networks showing improved accuracy. YOLO is a variant that replaces the softmax layer with a 1x1 convolutional layer and logistic function for multi-label classification.
The document provides an overview of LSTM (Long Short-Term Memory) networks. It first reviews RNNs (Recurrent Neural Networks) and their limitations in capturing long-term dependencies. It then introduces LSTM networks, which address this issue using forget, input, and output gates that allow the network to retain information for longer. Code examples are provided to demonstrate how LSTM remembers information over many time steps. Resources for further reading on LSTMs and RNNs are listed at the end.
The document provides an overview of key driver analysis techniques. It discusses the objectives of key driver analysis as determining the relative importance of predictor variables in predicting an outcome variable. It then reviews techniques like Shapley regression and Relative Importance Analysis. The document outlines 13 assumptions that should be checked when performing key driver analysis, such as whether the predictor and outcome variables are numeric. It provides options and recommendations for addressing violations of assumptions, like using generalized linear models for non-numeric variables. Two case studies applying the techniques to brand and cola preference data are presented.
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Natural language processing involves parsing text using a lexicon, categorization of parts of speech, and grammar rules. The parsing process involves determining the syntactic tree and label bracketing that represents the grammatical structure of sentences. Evaluation measures for parsing include precision, recall, and F1-score. Ambiguities from multiple word senses, anaphora, indexicality, metonymy, and metaphor make parsing challenging.
The document provides an introduction to word embeddings and two related techniques: Word2Vec and Word Movers Distance. Word2Vec is an algorithm that produces word embeddings by training a neural network on a large corpus of text, with the goal of producing dense vector representations of words that encode semantic relationships. Word Movers Distance is a method for calculating the semantic distance between documents based on the embedded word vectors, allowing comparison of documents with different words but similar meanings. The document explains these techniques and provides examples of their applications and properties.
This document discusses speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). It describes the process of feature extraction using MFCC which involves framing the speech signal, taking the Fourier transform of each frame, warping the frequencies using the mel scale, taking the logs of the powers at each mel frequency, and converting to cepstral coefficients. It then discusses feature matching techniques like vector quantization which clusters reference speaker features to create codebooks for comparison to unknown speakers. The document provides references for further reading on speech and speaker recognition techniques.
This document discusses building a next word prediction model using deep learning methods like LSTMs. It will take text as input, preprocess and tokenize the data, then build a deep learning model to predict the next word based on the previous words. Simple word prediction models use n-grams to calculate conditional word probabilities based on occurrence counts from text corpora. Bigram and trigram models are discussed as ways to predict the next word based on the previous one or two words in a sequence.
This document discusses gradient descent algorithms, feedforward neural networks, and backpropagation. It defines machine learning, artificial intelligence, and deep learning. It then explains gradient descent as an optimization technique used to minimize cost functions in deep learning models. It describes feedforward neural networks as having connections that move in one direction from input to output nodes. Backpropagation is mentioned as an algorithm for training neural networks.
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
This document discusses sentiment analysis using NLTK (Natural Language Toolkit) in Python. It begins with an overview of sentiment analysis and examples of determining sentiment from texts. Then it demonstrates the basics of using a sentiment dictionary to analyze sentences. It discusses challenges with real texts, like handling punctuation and splitting into sentences. NLTK tools for tokenization, sentence splitting, and counting positive and negative words are presented. Finally, it briefly introduces machine learning approaches to sentiment analysis using training data to build a model that can predict sentiment for new texts.
This document discusses genetic expression programming (GEP), an evolutionary algorithm related to genetic algorithms. It examines GEP's ability to model natural evolution through its inclusion of replicators, phenotypes, and a genotype-phenotype mapping. The document tests GEP on symbolic regression problems to analyze genetic neutrality by increasing gene length and number of genes. It finds GEP provides an ideal framework to study the effects of neutral mutations on evolution by allowing tight control over neutral regions in the genome.
VAW-GAN for disentanglement and recomposition of emotional elements in speechKunZhou18
- The document describes a framework for emotional voice conversion using VAW-GAN that can disentangle and recompose emotional elements in speech. It proposes using VAW-GAN with continuous wavelet transform to model prosody and decompose fundamental frequency into different time scales. Conditioning the decoder on fundamental frequency is shown to improve emotion conversion performance. Experiments demonstrate the effectiveness of the approach on an English emotional speech database.
A Study to Assess the Effectiveness of Planned Teaching Programme on Knowledg...ijtsrd
Suctioning is a common procedure performed by nurses to maintain the gas exchange, adequate oxygenation and alveolar ventilation in critical ill patients under mechanical ventilation and aim of this research is to provide knowledge regarding maintaining airway patency with suctioning care that will help in the implementation of the quality of nursing care, eventually it will lead to better results. The planned study is a pre experimental study to assess the effectiveness of planned teaching programme on knowledge regarding airway patency on patients with mechanical ventilator among the B.Sc. internship students of selected college of nursing at Moradabad. To assess the level of knowledge regarding maintaining airway patency in patients with mechanical ventilator among B.Sc. Nursing internship students. To assess the effectiveness of planned teaching programme in term of knowledge regarding airway patency among B.Sc. nursing internship students. The purpose of this study is to examine the association between knowledge and effectiveness regarding airway patency among B.Sc. Nursing internship demographic students and their selected partner variables. A pre experimental study was conducted among 86 participants, selected by non probability convenient sampling method. Demographic Performa and self structured questionnaire was used to collect the data from the B.Sc. internship students. Nafees Ahmed | Sana Usmani "A Study to Assess the Effectiveness of Planned Teaching Programme on Knowledge Regarding Maintaining Airway Patency in Patients with Mechanical Ventilator" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47917.pdf Paper URL: https://www.ijtsrd.com/medicine/nursing/47917/a-study-to-assess-the-effectiveness-of-planned-teaching-programme-on-knowledge-regarding-maintaining-airway-patency-in-patients-with-mechanical-ventilator/nafees-ahmed
This document discusses sentiment analysis on Twitter data using machine learning classifiers. It describes Twitter sentiment analysis as determining if a tweet is positive, negative, or neutral. Some challenges are that people express opinions complexly using sarcasm, irony, and slang. The document tests different classifiers like Naive Bayes and SVM on Twitter data preprocessed by tokenizing, extracting sentiment features, and part-of-speech tagging. It finds that extracting more features like sentiment and part-of-speech tags along with an SVM classifier achieves the best accuracy of 68% at determining tweet sentiment.
This document discusses methods for evaluating language models, including intrinsic and extrinsic evaluation. Intrinsic evaluation involves measuring a model's performance on a test set using metrics like perplexity, which is based on how well the model predicts the test set. Extrinsic evaluation embeds the model in an application and measures the application's performance. The document also covers techniques for dealing with unknown words like replacing low-frequency words with <UNK> and estimating its probability from training data.
Brief introduction on attention mechanism and its application in neural machine translation, especially in transformer, where attention was used to remove RNNs completely from NMT.
Speech recognition systems convert spoken words to text in real-time. They are used in dictation software and intelligent assistants. Design challenges include background noise, accent variations, and speed of speech. Speaker dependent systems recognize one voice, while speaker independent systems recognize any voice without training. Speech is broken into phonemes and a hidden Markov model identifies phonemes and language models recognize words. Components include signal analysis, acoustic and language models. Applications include healthcare, military, phones, and personal computers. Siri and Google Now are examples of intelligent assistants using these techniques.
Residual neural networks (ResNets) solve the vanishing gradient problem through shortcut connections that allow gradients to flow directly through the network. The ResNet architecture consists of repeating blocks with convolutional layers and shortcut connections. These connections perform identity mappings and add the outputs of the convolutional layers to the shortcut connection. This helps networks converge earlier and increases accuracy. Variants include basic blocks with two convolutional layers and bottleneck blocks with three layers. Parameters like number of layers affect ResNet performance, with deeper networks showing improved accuracy. YOLO is a variant that replaces the softmax layer with a 1x1 convolutional layer and logistic function for multi-label classification.
The document provides an overview of LSTM (Long Short-Term Memory) networks. It first reviews RNNs (Recurrent Neural Networks) and their limitations in capturing long-term dependencies. It then introduces LSTM networks, which address this issue using forget, input, and output gates that allow the network to retain information for longer. Code examples are provided to demonstrate how LSTM remembers information over many time steps. Resources for further reading on LSTMs and RNNs are listed at the end.
The document provides an overview of key driver analysis techniques. It discusses the objectives of key driver analysis as determining the relative importance of predictor variables in predicting an outcome variable. It then reviews techniques like Shapley regression and Relative Importance Analysis. The document outlines 13 assumptions that should be checked when performing key driver analysis, such as whether the predictor and outcome variables are numeric. It provides options and recommendations for addressing violations of assumptions, like using generalized linear models for non-numeric variables. Two case studies applying the techniques to brand and cola preference data are presented.
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Natural language processing involves parsing text using a lexicon, categorization of parts of speech, and grammar rules. The parsing process involves determining the syntactic tree and label bracketing that represents the grammatical structure of sentences. Evaluation measures for parsing include precision, recall, and F1-score. Ambiguities from multiple word senses, anaphora, indexicality, metonymy, and metaphor make parsing challenging.
The document provides an introduction to word embeddings and two related techniques: Word2Vec and Word Movers Distance. Word2Vec is an algorithm that produces word embeddings by training a neural network on a large corpus of text, with the goal of producing dense vector representations of words that encode semantic relationships. Word Movers Distance is a method for calculating the semantic distance between documents based on the embedded word vectors, allowing comparison of documents with different words but similar meanings. The document explains these techniques and provides examples of their applications and properties.
This document discusses speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). It describes the process of feature extraction using MFCC which involves framing the speech signal, taking the Fourier transform of each frame, warping the frequencies using the mel scale, taking the logs of the powers at each mel frequency, and converting to cepstral coefficients. It then discusses feature matching techniques like vector quantization which clusters reference speaker features to create codebooks for comparison to unknown speakers. The document provides references for further reading on speech and speaker recognition techniques.
This document discusses building a next word prediction model using deep learning methods like LSTMs. It will take text as input, preprocess and tokenize the data, then build a deep learning model to predict the next word based on the previous words. Simple word prediction models use n-grams to calculate conditional word probabilities based on occurrence counts from text corpora. Bigram and trigram models are discussed as ways to predict the next word based on the previous one or two words in a sequence.
This document discusses gradient descent algorithms, feedforward neural networks, and backpropagation. It defines machine learning, artificial intelligence, and deep learning. It then explains gradient descent as an optimization technique used to minimize cost functions in deep learning models. It describes feedforward neural networks as having connections that move in one direction from input to output nodes. Backpropagation is mentioned as an algorithm for training neural networks.
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
This document discusses sentiment analysis using NLTK (Natural Language Toolkit) in Python. It begins with an overview of sentiment analysis and examples of determining sentiment from texts. Then it demonstrates the basics of using a sentiment dictionary to analyze sentences. It discusses challenges with real texts, like handling punctuation and splitting into sentences. NLTK tools for tokenization, sentence splitting, and counting positive and negative words are presented. Finally, it briefly introduces machine learning approaches to sentiment analysis using training data to build a model that can predict sentiment for new texts.
This document discusses genetic expression programming (GEP), an evolutionary algorithm related to genetic algorithms. It examines GEP's ability to model natural evolution through its inclusion of replicators, phenotypes, and a genotype-phenotype mapping. The document tests GEP on symbolic regression problems to analyze genetic neutrality by increasing gene length and number of genes. It finds GEP provides an ideal framework to study the effects of neutral mutations on evolution by allowing tight control over neutral regions in the genome.
VAW-GAN for disentanglement and recomposition of emotional elements in speechKunZhou18
- The document describes a framework for emotional voice conversion using VAW-GAN that can disentangle and recompose emotional elements in speech. It proposes using VAW-GAN with continuous wavelet transform to model prosody and decompose fundamental frequency into different time scales. Conditioning the decoder on fundamental frequency is shown to improve emotion conversion performance. Experiments demonstrate the effectiveness of the approach on an English emotional speech database.
A Study to Assess the Effectiveness of Planned Teaching Programme on Knowledg...ijtsrd
Suctioning is a common procedure performed by nurses to maintain the gas exchange, adequate oxygenation and alveolar ventilation in critical ill patients under mechanical ventilation and aim of this research is to provide knowledge regarding maintaining airway patency with suctioning care that will help in the implementation of the quality of nursing care, eventually it will lead to better results. The planned study is a pre experimental study to assess the effectiveness of planned teaching programme on knowledge regarding airway patency on patients with mechanical ventilator among the B.Sc. internship students of selected college of nursing at Moradabad. To assess the level of knowledge regarding maintaining airway patency in patients with mechanical ventilator among B.Sc. Nursing internship students. To assess the effectiveness of planned teaching programme in term of knowledge regarding airway patency among B.Sc. nursing internship students. The purpose of this study is to examine the association between knowledge and effectiveness regarding airway patency among B.Sc. Nursing internship demographic students and their selected partner variables. A pre experimental study was conducted among 86 participants, selected by non probability convenient sampling method. Demographic Performa and self structured questionnaire was used to collect the data from the B.Sc. internship students. Nafees Ahmed | Sana Usmani "A Study to Assess the Effectiveness of Planned Teaching Programme on Knowledge Regarding Maintaining Airway Patency in Patients with Mechanical Ventilator" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47917.pdf Paper URL: https://www.ijtsrd.com/medicine/nursing/47917/a-study-to-assess-the-effectiveness-of-planned-teaching-programme-on-knowledge-regarding-maintaining-airway-patency-in-patients-with-mechanical-ventilator/nafees-ahmed
Speech Emotion Recognition Using Neural Networksijtsrd
Speech is the most natural and easy method for people to communicate, and interpreting speech is one of the most sophisticated tasks that the human brain conducts. The goal of Speech Emotion Recognition SER is to identify human emotion from speech. This is due to the fact that tone and pitch of the voice frequently reflect underlying emotions. Librosa was used to analyse audio and music, sound file was used to read and write sampled sound file formats, and sklearn was used to create the model. The current study looked on the effectiveness of Convolutional Neural Networks CNN in recognising spoken emotions. The networks input characteristics are spectrograms of voice samples. Mel Frequency Cepstral Coefficients MFCC are used to extract characteristics from audio. Our own voice dataset is utilised to train and test our algorithms. The emotions of the speech happy, sad, angry, neutral, shocked, disgusted will be determined based on the evaluation. Anirban Chakraborty "Speech Emotion Recognition Using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47958.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/47958/speech-emotion-recognition-using-neural-networks/anirban-chakraborty
Kun Zhou presents research on emotion modelling for speech generation. The document outlines three topics: (1) Seq2Seq emotion modelling to improve generalizability with limited training data, (2) Modelling emotion intensity and developing methods for its control, and (3) Mixed emotion modelling and synthesis to generate speech with combined emotions. The overall goal is to develop more expressive and controllable speech synthesis by teaching machines to better imitate human emotional speech.
A comparative analysis of classifiers in emotion recognition thru acoustic fea...Pravena Duplex
This document presents a comparative analysis of different classifiers for emotion recognition through acoustic features. It analyzes prosody features like energy and pitch as well as spectral features like MFCCs. Feature fusion, which combines prosody and spectral features, improves classification performance for LDA, RDA, SVM and kNN classifiers by around 20% compared to using features individually. Results on the Berlin and Spanish emotional speech databases show that RDA performs best as it avoids the singularity problem that affects LDA when dimensionality is high relative to the number of training samples.
BASIC ANALYSIS ON PROSODIC FEATURES IN EMOTIONAL SPEECHIJCSEA Journal
Speech is a rich source of information which gives not only about what a speaker says, but also about what the speaker’s attitude is toward the listener and toward the topic under discussion—as well as the speaker’s own current state of mind. Recently increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. The focus of this research work is to enhance man machine interface by focusing on user’s speech emotion. This paper gives the results of the basic analysis on prosodic features and also compares the prosodic features
of, various types and degrees of emotional expressions in Tamil speech based on the auditory impressions between the two genders of speakers as well as listeners. The speech samples consist of “neutral” speech as well as speech with three types of emotions (“anger”, “joy”, and “sadness”) of three degrees (“light”, “medium”, and “strong”). A listening test is also being conducted using 300 speech samples uttered by students at the ages of 19 -22 the ages of 19-22 years old. The features of prosodic parameters based on the emotional speech classified according to the auditory impressions of the subjects are analyzed. Analysis results suggest that prosodic features that identify their emotions and degrees are not only speakers’ gender dependent, but also listeners’ gender dependent.
This document provides an overview of a research talk on human-in-the-loop speech synthesis technology given by Yuki Saito from the University of Tokyo. The talk was organized in two parts, with the first part presented by Saito covering human-in-the-loop deep speaker representation learning and speaker adaptation for multi-speaker text-to-speech. Saito's research group at the University of Tokyo works on text-to-speech and voice conversion using deep learning techniques. Their recent work focuses on incorporating human listeners into the training process to learn speaker representations that better capture perceptual speaker similarity.
Emotion recognition based on the energy distribution of plosive syllablesIJECEIAES
We usually encounter two problems during speech emotion recognition (SER): expression and perception problems, which vary considerably between speakers, languages, and sentence pronunciation. In fact, finding an optimal system that characterizes the emotions overcoming all these differences is a promising prospect. In this perspective, we considered two emotional databases: Moroccan Arabic dialect emotional database (MADED), and Ryerson audio-visual database on emotional speech and song (RAVDESS) which present notable differences in terms of type (natural/acted), and language (Arabic/English). We proposed a detection process based on 27 acoustic features extracted from consonant-vowel (CV) syllabic units: \ba, \du, \ki, \ta common to both databases. We tested two classification strategies: multiclass (all emotions combined: joy, sadness, neutral, anger) and binary (neutral vs. others, positive emotions (joy) vs. negative emotions (sadness, anger), sadness vs. anger). These strategies were tested three times: i) on MADED, ii) on RAVDESS, iii) on MADED and RAVDESS. The proposed method gave better recognition accuracy in the case of binary classification. The rates reach an average of 78% for the multi-class classification, 100% for neutral vs. other cases, 100% for the negative emotions (i.e. anger vs. sadness), and 96% for the positive vs. negative emotions.
A Review Paper on Speech Based Emotion Detection Using Deep LearningIRJET Journal
This document reviews techniques for speech-based emotion detection using deep learning. It discusses how deep learning techniques have been proposed as an alternative to traditional methods for speech emotion recognition. Feature extraction is an important part of speech emotion recognition, and deep learning can help minimize the complexity of extracted features. The document surveys related work on speech emotion recognition using techniques like deep neural networks, convolutional neural networks, recurrent neural networks, and more. It examines the limitations of current approaches and the potential for deep learning to improve speech-based emotion detection.
Deep Learning for Natural Language ProcessingParrotAI
The document discusses deep learning approaches for natural language processing (NLP). It introduces NLP and common applications. Word representations like one-hot and distributed representations are covered, with a focus on Word2Vec models. Recurrent neural networks (RNNs) are described as useful for sequential language data, including variants like bidirectional RNNs and applications such as neural machine translation and sentiment analysis.
Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decom...BIJIAM Journal
This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition (EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network (DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train the DNN model. Finally, a trained model that could recognize the emotional signals is then used to identify emotions in speeches. Experimental results reveal that the proposed method is effective.
A critical insight into multi-languages speech emotion databasesjournalBEEI
With increased interest of human-computer/human-human interactions, systems deducing and identifying emotional aspects of a speech signal has emerged as a hot research topic. Recent researches are directed towards the development of automated and intelligent analysis of human utterances. Although numerous researches have been put into place for designing systems, algorithms, classifiers in the related field; however the things are far from standardization yet. There still exists considerable amount of uncertainty with regard to aspects such as determining influencing features, better performing algorithms, number of emotion classification etc. Among the influencing factors, the uniqueness between speech databases such as data collection method is accepted to be significant among the research community. Speech emotion database is essentially a repository of varied human speech samples collected and sampled using a specified method. This paper reviews 34 `speech emotion databases for their characteristics and specifications. Furthermore critical insight into the imitational aspects for the same have also been highlighted.
This paper introduces new features based on histograms of MFCC extracted from audio files to improve emotion recognition from speech. Experimental results on the Berlin and PAU databases using SVM and Random Forest classifiers show the proposed features achieve better classification results than current methods. Detailed analysis is provided on speech type (acted vs natural) and gender.
SENTIMENT ANALYSIS IN MYANMAR LANGUAGE USING CONVOLUTIONAL LSTM NEURAL NETWORKijnlc
In recent years, there has been an increasing use of social media among people in Myanmar and writing review on social media pages about the product, movie, and trip are also popular among people. Moreover, most of the people are going to find the review pages about the product they want to buy before deciding whether they should buy it or not. Extracting and receiving useful reviews over interesting products is very important and time consuming for people. Sentiment analysis is one of the important processes for extracting useful reviews of the products. In this paper, the Convolutional LSTM neural network architecture is proposed to analyse the sentiment classification of cosmetic reviews written in Myanmar Language. The paper also intends to build the cosmetic reviews dataset for deep learning and sentiment lexicon in Myanmar Language.
Sentiment Analysis In Myanmar Language Using Convolutional Lstm Neural Networkkevig
In recent years, there has been an increasing use of social media among people in Myanmar and writing
review on social media pages about the product, movie, and trip are also popular among people. Moreover,
most of the people are going to find the review pages about the product they want to buy before deciding
whether they should buy it or not. Extracting and receiving useful reviews over interesting products is very
important and time consuming for people. Sentiment analysis is one of the important processes for extracting
useful reviews of the products. In this paper, the Convolutional LSTM neural network architecture is
proposed to analyse the sentiment classification of cosmetic reviews written in Myanmar Language. The
paper also intends to build the cosmetic reviews dataset for deep learning and sentiment lexicon in Myanmar
Language.
Predicting the Level of Emotion by Means of Indonesian Speech SignalTELKOMNIKA JOURNAL
This document summarizes a study that aimed to evaluate how well Mel-Frequency Cepstral Coefficients (MFCC) features extracted from Indonesian speech signals relate to four emotions: happy, sad, angry, and fear. Nearly 300 speech signals were collected from actors speaking Indonesian sentences with different emotions. Using support vector machine classification, the study found that the Teager energy feature and the first MFCC coefficient were most crucial for prediction, achieving 86% accuracy. Additional initial MFCC features increased accuracy slightly, but more than four features had negligible effects.
Deep neural networks have shown recent promise in many language-related tasks such as the modelling of
conversations. We extend RNN-based sequence to sequence models to capture the long-range discourse
across many turns of conversation. We perform a sensitivity analysis on how much additional context
affects performance, and provide quantitative and qualitative evidence that these models can capture
discourse relationships across multiple utterances. Our results show how adding an additional RNN layer
for modelling discourse improves the quality of output utterances and providing more of the previous
conversation as input also improves performance. By searching the generated outputs for specific
discourse markers, we show how neural discourse models can exhibit increased coherence and cohesion in
conversations.
The document discusses an experiment that analyzed cultural differences in emotion recognition through electroencephalography (EEG) brain scans. Researchers recorded EEG data from Serbian and Chinese participants as they listened to audio clips expressing different emotions. They aimed to determine if language understanding impacts emotion perception and if different nationalities experience emotions differently. Classification analysis found brain signals in response to different emotions were not considerably different, suggesting some commonality in how emotions are experienced.
The document discusses an experiment that analyzed cultural differences in emotion recognition through electroencephalography (EEG) brain scans. Researchers recorded EEG data from Serbian and Chinese participants as they listened to audio clips expressing different emotions. They aimed to determine if language understanding impacts emotion perception and if different nationalities experience emotions differently based on brain activity patterns. Classification accuracy results suggested brain signals in response to different emotions were not considerably different, regardless of nationality.
Upgrading the Performance of Speech Emotion Recognition at the Segmental Level IOSR Journals
This document presents research on improving the accuracy of automatic speech emotion recognition using minimal inputs and features. The researchers used only the vowel formants from English speech recordings of 10 female speakers producing neutral and 6 basic emotions. They analyzed the vowel formants using statistical analysis and 3 classifiers to identify the best performing formants. An artificial neural network using the selected formant values achieved 95.6% accuracy in classifying emotions, higher than previous studies. The approach requires fewer features and less complex processing while achieving good recognition rates.
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...PIMR BHOPAL
Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
1. NUS Presentation Title 2001
Seen and Unseen emotional style transfer for voice
conversion with a new emotional speech dataset
Kun Zhou1, Berrak Sisman2, Rui Liu2, Haizhou Li1
1National University of Singapore
2Singapore University of Technology and Design
2. NUS Presentation Title 2001
Outline
1. Introduction
2. Related Work
3. Contributions
4. Proposed Framework
5. Experiments
6. Conclusions
3. NUS Presentation Title 2001
Outline
1. Introduction
2. Related Work
3. Contributions
4. Proposed Framework
5. Experiments
6. Conclusions
4. NUS Presentation Title 2001
1. Introduction
1.1 Emotional voice conversion
Emotional voice conversion aims to transfer the emotional style of an utterance
from one to another, while preserving the linguistic content and speaker identity.
Applications: emotional text-to-speech, conversational agents
Compared with speaker voice conversion:
1) Speaker identity is mainly determined by voice quality;
-- speaker voice conversion mainly focuses on spectrum conversion
2) Emotion is the interplay of multiple acoustic attributes concerning both spectrum
and prosody.
-- emotional voice conversion focuses on both spectrum and prosody conversion
5. NUS Presentation Title 2001
1. Introduction
1.2 Current Challenges
1) Non-parallel training
Parallel data is difficult and expensive to collect in real life;
2) Emotional prosody modelling
Emotional prosody is hierarchical in nature, and perceived at phoneme,
word, and sentence level;
3) Unseen emotion conversion
Existing studies use discrete emotion categories, such as one-hot label to
remember a fixed set of emotional styles.
Our focus
Our focus
6. NUS Presentation Title 2001
Outline
1. Introduction
2. Related Work
3. Contributions
4. Proposed Framework
5. Experiments
6. Conclusions
7. NUS Presentation Title 2001
2. Related Work
2.1 Emotion representation
1) Categorical representation
Example: Ekman’s six basic emotions theory[1]:
i.e., anger, disgust, fear, happiness, sadness and surprise
2) Dimensional representation
Example: Russell’s circumplex model[2]:
i.e., arousal, valence, and dominance
[1] Paul Ekman, “An argument for basic emotions,” Cognition & emotion, 1992
[2] James A Russell, “A circumplex model of affect.,” Journal of personality and social psychology, vol. 39, no. 6, pp. 1161, 1980.
8. NUS Presentation Title 2001
2. Related Work
2.2 Emotional descriptor
1) One-hot emotion labels
2) Expert-crafted features such as
openSMILE [3]
3) Deep emotional features:
[3] Florian Eyben, Martin Wöllmer, and Björn Schuller, Opensmile: the munich versatile and fast open-source audio feature extractor, Proceedings of the
18th acm international conference on multimedia, 2010, pp. 1459–1462
-- data-driven, less dependent on human
knowledge, more suitable for emotional
style transfer…
9. NUS Presentation Title 2001
2. Related Work
2.3 Variational Auto-encoding Wasserstein Generative Adversarial
Network (VAW-GAN)[4]
Fig 2. An illustration of VAW-GAN framework.
- Based on variational auto-encoder
(VAE)
- Consisting of three components:
Encoder: to learn a latent code from the
input;
Generator: to reconstruct the input from the
latent code and the inference code;
Discriminator: to judge the reconstructed
input whether real or not;
- First introduced in speaker voice
conversion in 2017
[4] Chin-Cheng Hsu, Hsin-Te Hwang, Yi-Chiao Wu, Yu Tsao, and HsinMin Wang, “Voice conversion from unaligned
corpora using variational autoencoding wasserstein generative adversarial networks,” in Proc. Interspeech, 2017.
10. NUS Presentation Title 2001
Outline
1. Introduction
2. Related Work
3. Contributions
4. Proposed Framework
5. Experiments
6. Conclusions
11. NUS Presentation Title 2001
3. Contributions
1. One-to-many emotional style transfer framework
2. Does not require parallel training data;
3. Use speech emotion recognition model to describe the emotional style;
4. Publicly available multi-lingual and multi-speaker emotional speech
corpus (ESD), that can be used for various speech synthesis tasks;
To our best knowledge, this is the first reported study on emotional style transfer
for unseen emotion.
12. NUS Presentation Title 2001
Outline
1. Introduction
2. Related Work
3. Contributions
4. Proposed Framework
5. Experiments
6. Conclusions
13. NUS Presentation Title 2001
4. Proposed Framework
1) Stage I: Emotion Descriptor Training
2) Stage II: Encoder-Decoder Training with VAW-GAN
3) Stage III: Run-time Conversion
Fig. 3. The training phase of the proposed DeepEST framework. Blue boxes represent the networks
that involved in the training, and the red boxes represent the networks that are already trained.
14. NUS Presentation Title 2001
4. Proposed Framework
Fig. 3. The training phase of the proposed DeepEST framework. Blue boxes represent the networks
that involved in the training, and the red boxes represent the networks that are already trained.
1) Emotion Descriptor Training
- A pre-trained SER model is used to extract deep emotional features from the
input utterance.
- The utterance-level deep emotional features are thought to describe different
emotions in a continuous space.
15. NUS Presentation Title 2001
4. Proposed Framework
Fig. 3. The training phase of the proposed DeepEST framework. Blue boxes represent the networks
that involved in the training, and the red boxes represent the networks that are already trained.
2) Encoder-Decoder Training with VAW-GAN
- The encoder learns to generate emotion-independent latent representation z
from the input features
- The decoder learns to reconstruct the input features with latent representation,
F0 contour, and deep emotional features;
- The discriminator learns to determine whether the reconstructed features real
or not;
16. NUS Presentation Title 2001
4. Proposed Framework
Fig. 3. The training phase of the proposed DeepEST framework. Blue boxes represent the networks
that involved in the training, and the red boxes represent the networks that are already trained.
3) Run-time Conversion
- The pre-trained SER generates the deep emotional features from the reference
set;
- We then concatenate the deep emotional features with the converted F0 and
emotion-independent z as the input to the decoder;
17. NUS Presentation Title 2001
Outline
1. Introduction
2. Related Work
3. Contributions
4. Proposed Framework
5. Experiments
6. Conclusions
18. NUS Presentation Title 2001
5. Experiments
5.1 Emotional Speech Dataset -- ESD
1) The dataset consists of 350 parallel utterances with an average duration
of 2.9 seconds spoken by 10 native English and 10 native Mandarin
speakers;
2) For each language, the dataset consists of 5 male and 5 female speakers
in five emotions:
a) happy, b) sad, c) neutral, d) angry, and e) surprise
3) ESD dataset is publicly available at:
https://github.com/ HLTSingapore/Emotional-Speech-Data
To our best knowledge, this is the first parallel emotional voice
conversion dataset in a multi-lingual and multi-speaker setup.
19. NUS Presentation Title 2001
5. Experiments
5.2 Experimental setup
1) 300-30-20 utterances (training-reference-test set);
2) One universal model that takes neutral, happy and sad utterances as input;
3) SER is trained on a subset of IEMOCAP[5], with four emotion types (happy, angry,
sad and neutral)
4) We conduct emotion conversion from neutral to seen emotional states (happy, sad)
to unseen emotional states (angry).
5) Baseline: VAW-GAN-EVC [6]: only capable of one-to-one conversion and generate
seen emotions;
Proposed: DeepEST: one-to-many conversion, seen and unseen emotion generation
[5] Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang,
Sungbok Lee, and Shrikanth S Narayanan, “Iemocap: Interactive emotional dyadic motion capture database,” Language
resources and evaluation, vol. 42, no. 4, pp. 335, 2008.
[6] Kun Zhou, Berrak Sisman, Mingyang Zhang, and Haizhou Li, “Converting Anyone’s Emotion: Towards Speaker-
Independent Emotional Voice Conversion,” in Proc. Interspeech 2020, 2020, pp. 3416–3420.
20. NUS Presentation Title 2001
5. Experiments
5.3 Objective Evaluation
We calculate MCD [dB] to measure the spectral distortion
Proposed DeepEST outperforms the baseline framework for seen emotion
conversion and achieves comparable results for unseen emotion conversion.
21. NUS Presentation Title 2001
5. Experiments
5.4 Subjective Evaluation
1) MOS listening experiments
2) AB preference test
3) XAB preference test
To evaluate speech quality:
To evaluate emotion similarity:
Proposed DeepEST achieves competitive
results for both seen and unseen emotions
23. NUS Presentation Title 2001
Outline
1. Introduction
2. Related Work
3. Contributions
4. Proposed Framework
5. Experiments
6. Conclusions
24. NUS Presentation Title 2001
6. Conclusion
1. We propose a one-to-many emotional style transfer framework based on
VAW-GAN without the need for parallel data;
2. We propose to leverage deep emotional features from speech emotion
recognition to describe the emotional prosody in a continuous space;
3. We condition the decoder with controllable attributes such as deep emotional
features and F0 values;
4. We achieve competitive results for both seen and unseen emotions over the
baselines;
5. We introduce and release a new emotional speech dataset, ESD, that can be
used in speech synthesis and voice conversion.