The MOBILE SDS was developed as a remote system on a mobile phone . It uses Google server for recognition and Google text to speech for speech synthesis provided with Android system . Dialogue understanding , dialogue managing and text generating modules reside on a remote computer . Four factors were found to be important for characterizing the glottal excitations for the four voice types : the glottal pulse width , the glottal pulse skewness , the abruptness of glottal closure , and the turbulent noise component . The significance of these factors for voice synthesis was studied and a new voice source model that accounted for certain physiological aspects of vocal fold motion was developed and tested using speech synthesis . Perceptual listening tests were conducted to evaluate the auditory effects of the source model parameters upon synthesized speech . But the overlap-add algorithm is well known for high computational complexity and the peak-picking algorithm can not track the transient and syllabic variation well . In this letter , both algorithms are applied to speech analysis/synthesis . Peaks are picked in the curve of power spectral density for speech signal ; the frequencies corresponding to these peaks are arranged according to the descending orders of their corresponding power spectral densities . Speech analysis-synthesis is an effective method for low bit-rate speech coding . However , it has been pointed out that system performance degrades as noise is added to the input speech . Text-to-speech system ( TTS ) , known also as speech synthesizer , is one of the important technology in the last years due to the expanding field of applications . Several works on speech synthesizer have been made on English and French , whereas many other languages , including Arabic , have been recently taken into consideration . The area of Arabic speech synthesis has not sufficient progress and it is still in its first stage with a low speech quality . This paper presents a novel technique of characterizing and modeling workloads at the instruction level for realistic multimedia applications using hardware performance counters . A variety of instruction counts are collected from some multimedia applications , such as RealPlayer , GSM Vocoder , MPEG encoder/decoder , and speech synthesizer . These instruction counts can be used to form a set of abstract characteristic parameters directly related to a processor`s architectural features . In this paper , a new approach to speech signal synthesis is proposed . The basic idea is to represent a speech signal as the impulse response of a pole-zero digital filter . It is hierarchically organised . The generative grammars for model speech signal synthesis are not used as a feedback in speech recognition and understanding process . Instead of the latter , significant decisions , but under simplified conditions , at all levels of speech signal processing hierarchy are introduced . 115(5 ) , 2543 ( 2004 ) ] , has been used in both research and teaching . In its latest version , several new features are included , which are speech sound synthesis with source model options , formant sensitivity function calculation , susceptance plots calculation useful particularly for nasalized vowel analysis , and addition of a new set of area function data for liquid sounds extracted from MR ( magnetic resonance ) images . These new features along with the user‐friendly interface significantly enhance the usability of VTAR for both teaching and research purposes . In surveying geometrical synthesis the paper concentrates especially on movement parametrisation and trajectory generation . Central topics in speech sound synthesis are the transmission line method . Finally the possible future directions are given . To expand its utilization over platforms with different computational capacities , this paper described a real implementation of Thai TTS system for hand-held devices . The system consisted of two parts : the text analysis part and the speech synthesis part . To overcome an issue on the limited resources of the devices , a rule-based technique was chosen for the text analysis while the speech synthesis part was deployed in a model-based approach , specifically hidden Markov models , rather to the concatenative one . In this paper , we propose a hybrid approach for a person with an articulation disorder , using two models of a physically unimpaired person and a person with an articulation disorder to generate an intelligible voice while preserving the speaker 's individuality ( with an articulation disorder ) . Our method has two processes - the speech synthesis part and voice conversion part . Speech synthesis is employed for obtaining a speech signal ( of a physically unimpaired person ) , where a large amount of training data of a physically unimpaired person is used . We add a large number of categories of non-standard words ( NSWs ) that we believe a robust real-world text normalization system will have to be able to process . Our new categories are based upon empirical findings encountered while building text normalization systems across many languages , for both speech recognition and speech synthesis purposes . We believe our new taxonomy is useful both for ensuring high coverage when writing manual grammars , as well as for eliciting training data to build machine learning-based text normalization systems . One of the interesting things that gives the edge to a voice as a communication medium over anything else is that it ’s faster and of course more natural . Today , more and more devices have built-in speech recognition and speech synthesis capabilities . In fact , there are a lot of systems already available and in use that utilize these smart systems and interactions . MOOC-bot is using the popular Artificial Intelligence Markup Language ( AIML ) to develop its knowledge base , leverage from AIML capability to deliver appropriate responses and can be quickly adapted to new knowledge domains . The system architecture of MOOC-bot consists of knowledge base along with AIML interpreter , chat interface , MOOCs website and Web Speech API to provide speech recognition and speech synthesis capability . The initial MOOC-bot prototype has the general knowledge from the past Loebner Prize winner - ALICE , frequent asked questions , and a content offered by Universiti Teknikal Malaysia Melaka ( UTeM ) . Recurrent neural networks ( RNNs ) are powerful sequence density estimators that can use long contexts to make predictions . They have achieved tremendous success in ( conditional ) sequence modelling tasks such as language modelling , machine translation and speech recognition . Generative models of sequences can apply factorization via the product rule to perform density estimation of the sequence , RNNs can model sequences with the above factorization by using a hidden state to summarize past inputs . As revealed in , the interactions with useless features may introduce noises and degrade the performance . In recent years , deep neural networks ( DNNs ) have become successful in computer vision , speech recognition , and natural language processing with their great power of feature representation learning . It is promising to exploit DNNs to learn sophisticated and selective feature interactions . Recent work has shown that recurrent neural networks ( RNNs ) can deliver excellent performance in many such tasks when trained to predict the next output token given the input and previous tokens . This approach has been applied successfully in machine translation sutskever2014sequence , bahdanau2015neural , caption generation kiros2014unifying , donahue2015long , vinyals2015show , xu2015show , karpathy2015deep , and speech recognition chorowski2015attention , chan2015listen . The standard way to train RNNs to generate sequences is to maximize the log - likelihood of the “ correct ” token given a history of the previous “ correct ” ones , an approach often called teacher forcing . Recent researches in speech synthesis are mainly focused on naturalness , and the emotional speech synthesis becomes one of the highlighted research topics . Although quite a many studies on emotional speech in English or Japanese have been addressed , the studies in Korean can seldom be found . The classifications of features involve the training of various emotional models to perform the classification appropriately . Another important aspect to be considered in emotional speech recognition is the database used for training the models [ 1 ] . As for the benchmark , we use the frameworks of the second audio/visual emotion challenge and the Interspeech 2012 speaker trait challenge . Also proposed in this work is the use of part-of-speech tags as syntactic features of speech for emotional speech recognition . Results of this work show that despite the relatively small number of features that is used for the modeling tasks , generalization capability of the suggested models is comparable to those of other models that use thousands of features and more elaborate learning algorithms . Emotional speech production has been previously studied using fleshpoint tracking data in speaker-specific experiment setups . The present study introduces a real-time magnetic resonance imaging database of emotional speech production from 10 speakers and presents articulatory analysis results of speech emotional expression using the database . Midsagittal vocal tract parameters ( midsagittal distances and the vocal tract length ) were parameterized based on a two-dimensional grid-line system , using image segmentation software . To do this , we are trying to discuss what is essential in production and perception of non-linguistic information , to clarify biological common features among humans independent of languages , nations and cultures , and to apply these common features to man-machine communication as well as human-human communication . In this paper , as the results of our activities in perception of nonlinguistic information , we introduce a multi-layer emotional speech perception model and present some results of emotional speech synthesis and recognition using the model . The 11 papers in this special issue can be categorized into five groups : Emotional speech synthesis and recognition ; affective video content analysis ; facial expressions and head movements ; affect analysis in small groups ; and audio-visual affective corpus . This paper proposes an analysis method for emotional voice and application to emotional voice synthesis in man-machine interaction . The analysis is based on a high resolution time-frequency method and the clustering technique of four types of basic emotion . The chapter introduces prosody conversion models for emotional speech generation by using a Gaussian Mixture Model ( GMM ) , and a Classification And Regression Tree ( CART ) model . Unlike the rule-based or linear modification method , the GMM and CART models try to map the subtle prosody distributions between neutral and emotional speech . Moreover , and in spite of data availability , we also show how there are emotions ( sadness and anger ) that are clearly identified in the automatic experiments , for both languages , showing a reasonably clear " language-independent " behavior of such emotions . Finally , we show how psychological considerations related to the emotional speech generation process can be somehow related to our experimental results , suggesting that previously identifying such psychological features may greatly help the development of high quality " universal " emotional speech identification systems . Advances in emotional speech recognition and synthesis essentially rely on the availability of annotated emotional speech corpora . As a low resource language , the Thai language critically lacks corpora of emotional speech , although a few corpora have been constructed for speech recognition and synthesis . Experiments carried out on a French speech corpus show an improvement in the quality of speech synthesis when pronunciation models are included in the phonetization process . Appart from improving TTS quality , the presented pronunciation adaptation method also brings interesting perspectives in terms of expressive speech synthesis . This paper analyzes and compares spectral properties ( first three formants position and spectral flatness measure values ) and prosodic parameters ( F0 and energy , microintonation and jitter ) of male and female acted emotional speech in Czech and Slovak languages . Statistical results and values of parameter ratios will be used for modification of the text-to-speech ( TTS ) system enabling expressive speech production with male / female voices , based on cepstral speech description . The workshop is organized under the auspices of the SSPNet , the FP7 European Network of Excellence on Social Signal Processing ( EC FP7 grant agreement no. 231287 ) , and continues the tradition of the previous SSPW workshops by maintaining the high standard set by its predecessors . Main topics discussed during the SSPW workshop series include the following : Social Intelligence , Social Cognition and Social Behavior Modeling Facial behavior analysis and synthesis in social interactions Expressive speech analysis and synthesis in social interactions Human gesture and action recognition and synthesis in social interactions Multimodal human behavior analysis and synthesis in social interactions Perceptual , multimodal , and socially-aware user interfaces Socially-adept Embodied Conversational Agents Data Mining , Machine Learning , Information Retrieval , Artificial Intelligence in Social Contexts Databases for training and testing Socially-aware computing and applications ( reality mining , implicit multimedia tagging , etc . ) The solution is to synthesize realistic 3D talking faces by learning from facial motion capture data . This dissertation addresses three critical parts of realistic talking face synthesis : realistic eye motion synthesis , natural head motion synthesis , and expressive speech animation synthesis . A texture-synthesis based approach is presented to simultaneously synthesize realistic eye gaze and blink motion , accounting for any possible correlations between the two . Modeling global expressiveness in speech synthesis . We present in this paper some modeling issues for expressive speech generation from a text necessarily determined by some extrinsic characteristics for natural output ( such as the textual genre , the reader 's profile and the discursive situation ) . We then argue for a five-level model for main speech expressiveness , which is in the very basis of symbolic coding , formal transformation and physical generation of some prosodic patterns one meets in today 's oral French . Instead , the paper presents a personal account of some of the problems facing researchers who wish to study the speech characteristics associated with different emotions . It approaches the issue from the standpoint of speech technology , rather than that of psychology , and describes work planned under a forthcoming JST-funded five-year project for the study of ` expressive speech phenomena ’ which will include the production of a large-scale emotional-speech database . Rather than present new facts or data , the paper sets out some topics for discussion and raises questions ; in the hope that some of the issues may be resolved during the three days of the workshop . Expressive Speech Recognition and Synthesis as Enabling Technologies for Affective Robot-Child Communication.- Embodied Conversational Agents : Computing and Rendering Realistic Gaze Patterns.- DBN Based Models for Audio-Visual Speech Analysis and Recognition.- An Extensive Method to Detect the Image Digital Watermarking Based on the Known Template.- Fast Mode Decision Algorithm in H.263+/H.264 Intra Transcoder.- Binary Erasure Codes for Packet Transmission Subject to Correlated Erasures.- Image Desynchronization for Secure Collusion-Resilient Fingerprint in Compression Domain.- A Format-Compliant Encryption Framework for JPEG2000 Image Code-Streams in Broadcasting Applications.- Euclidean Distance Transform of Digital Images in Arbitrary Dimensions.- JPEG2000 Steganography Possibly Secure Against Histogram-Based Attack.- Perceptual Depth Estimation from a Single 2D Image Based on Visual Perception Theory.- A System for Generating Personalized Virtual News.- Image Fingerprinting Scheme for Print-and-Capture Model.- 16x16 Integer Cosine Transform for HD Video Coding.- Heegard-Berger Video Coding Using LMMSE Estimator.- Real-Time BSD-Driven Adaptation Along the Temporal Axis of H.264/AVC Bitstreams.- Optimal Image Watermark Decoding.- Diagonal Discrete Cosine Transforms for Image Coding.- Synthesizing Variational Direction and Scale Texture on Planar Region.- Fast Content-Based Image Retrieval Based on Equal-Average K-Nearest-Neighbor Search Schemes.- Characterizing User Behavior to Improve Quality of Streaming Service over P2P Networks.- Interacting Activity Recognition Using Hierarchical Durational-State Dynamic Bayesian Network.- Improving the Image Retrieval Results Via Topic Coverage Graph.- Relevance Feedback for Sketch Retrieval Based on Linear Programming This paper presents our recent and current work on expressive speech synthesis and recognition as enabling technologies for affective robot-child interaction . We show that current expression recognition systems could be used to discriminate between several archetypical emotions , but also that the old adage ” there ’s no data like more data ” is more than ever valid in this field . Abstract Emotion is considered to be an essential element in the performance of human-computer interactions . In expressive synthesis speech , it is important to generate emotional speech that reflects subtle and complex emotional states . However , there has been limited research on how to effectively synthesize emotional speech using different levels of emotion strength with intuitive control , which is difficult to be modeled effectively . This chapter contains sections titled : Emotion in Human Speech , Expressive Voice Synthesis , Implementation Overview , Kismet 's Expressive Utterances , Real-Time Lip Synchronization and Facial Animation , Limitations and Extensions , Summary
The MOBILE SDS was developed as a remote system on a mobile phone . It uses Google server for recognition and Google text to speech for speech synthesis provided with Android system . Dialogue understanding , dialogue managing and text generating modules reside on a remote computer .
Four factors were found to be important for characterizing the glottal excitations for the four voice types : the glottal pulse width , the glottal pulse skewness , the abruptness of glottal closure , and the turbulent noise component . The significance of these factors for voice synthesis was studied and a new voice source model that accounted for certain physiological aspects of vocal fold motion was developed and tested using speech synthesis . Perceptual listening tests were conducted to evaluate the auditory effects of the source model parameters upon synthesized speech .
But the overlap-add algorithm is well known for high computational complexity and the peak-picking algorithm can not track the transient and syllabic variation well . In this letter , both algorithms are applied to speech analysis/synthesis . Peaks are picked in the curve of power spectral density for speech signal ; the frequencies corresponding to these peaks are arranged according to the descending orders of their corresponding power spectral densities .
Speech analysis-synthesis is an effective method for low bit-rate speech coding . However , it has been pointed out that system performance degrades as noise is added to the input speech .
Text-to-speech system ( TTS ) , known also as speech synthesizer , is one of the important technology in the last years due to the expanding field of applications . Several works on speech synthesizer have been made on English and French , whereas many other languages , including Arabic , have been recently taken into consideration . The area of Arabic speech synthesis has not sufficient progress and it is still in its first stage with a low speech quality .
This paper presents a novel technique of characterizing and modeling workloads at the instruction level for realistic multimedia applications using hardware performance counters . A variety of instruction counts are collected from some multimedia applications , such as RealPlayer , GSM Vocoder , MPEG encoder/decoder , and speech synthesizer . These instruction counts can be used to form a set of abstract characteristic parameters directly related to a processor`s architectural features .
In this paper , a new approach to speech signal synthesis is proposed . The basic idea is to represent a speech signal as the impulse response of a pole-zero digital filter .
It is hierarchically organised . The generative grammars for model speech signal synthesis are not used as a feedback in speech recognition and understanding process . Instead of the latter , significant decisions , but under simplified conditions , at all levels of speech signal processing hierarchy are introduced .
115(5 ) , 2543 ( 2004 ) ] , has been used in both research and teaching . In its latest version , several new features are included , which are speech sound synthesis with source model options , formant sensitivity function calculation , susceptance plots calculation useful particularly for nasalized vowel analysis , and addition of a new set of area function data for liquid sounds extracted from MR ( magnetic resonance ) images . These new features along with the user‐friendly interface significantly enhance the usability of VTAR for both teaching and research purposes .
In surveying geometrical synthesis the paper concentrates especially on movement parametrisation and trajectory generation . Central topics in speech sound synthesis are the transmission line method . Finally the possible future directions are given .
To expand its utilization over platforms with different computational capacities , this paper described a real implementation of Thai TTS system for hand-held devices . The system consisted of two parts : the text analysis part and the speech synthesis part . To overcome an issue on the limited resources of the devices , a rule-based technique was chosen for the text analysis while the speech synthesis part was deployed in a model-based approach , specifically hidden Markov models , rather to the concatenative one .
In this paper , we propose a hybrid approach for a person with an articulation disorder , using two models of a physically unimpaired person and a person with an articulation disorder to generate an intelligible voice while preserving the speaker 's individuality ( with an articulation disorder ) . Our method has two processes - the speech synthesis part and voice conversion part . Speech synthesis is employed for obtaining a speech signal ( of a physically unimpaired person ) , where a large amount of training data of a physically unimpaired person is used .
We add a large number of categories of non-standard words ( NSWs ) that we believe a robust real-world text normalization system will have to be able to process . Our new categories are based upon empirical findings encountered while building text normalization systems across many languages , for both speech recognition and speech synthesis purposes . We believe our new taxonomy is useful both for ensuring high coverage when writing manual grammars , as well as for eliciting training data to build machine learning-based text normalization systems .
One of the interesting things that gives the edge to a voice as a communication medium over anything else is that it ’s faster and of course more natural . Today , more and more devices have built-in speech recognition and speech synthesis capabilities . In fact , there are a lot of systems already available and in use that utilize these smart systems and interactions .
MOOC-bot is using the popular Artificial Intelligence Markup Language ( AIML ) to develop its knowledge base , leverage from AIML capability to deliver appropriate responses and can be quickly adapted to new knowledge domains . The system architecture of MOOC-bot consists of knowledge base along with AIML interpreter , chat interface , MOOCs website and Web Speech API to provide speech recognition and speech synthesis capability . The initial MOOC-bot prototype has the general knowledge from the past Loebner Prize winner - ALICE , frequent asked questions , and a content offered by Universiti Teknikal Malaysia Melaka ( UTeM ) .
Recurrent neural networks ( RNNs ) are powerful sequence density estimators that can use long contexts to make predictions . They have achieved tremendous success in ( conditional ) sequence modelling tasks such as language modelling , machine translation and speech recognition . Generative models of sequences can apply factorization via the product rule to perform density estimation of the sequence , RNNs can model sequences with the above factorization by using a hidden state to summarize past inputs .
As revealed in , the interactions with useless features may introduce noises and degrade the performance . In recent years , deep neural networks ( DNNs ) have become successful in computer vision , speech recognition , and natural language processing with their great power of feature representation learning . It is promising to exploit DNNs to learn sophisticated and selective feature interactions .
Recent work has shown that recurrent neural networks ( RNNs ) can deliver excellent performance in many such tasks when trained to predict the next output token given the input and previous tokens . This approach has been applied successfully in machine translation sutskever2014sequence , bahdanau2015neural , caption generation kiros2014unifying , donahue2015long , vinyals2015show , xu2015show , karpathy2015deep , and speech recognition chorowski2015attention , chan2015listen . The standard way to train RNNs to generate sequences is to maximize the log - likelihood of the “ correct ” token given a history of the previous “ correct ” ones , an approach often called teacher forcing .
Recent researches in speech synthesis are mainly focused on naturalness , and the emotional speech synthesis becomes one of the highlighted research topics . Although quite a many studies on emotional speech in English or Japanese have been addressed , the studies in Korean can seldom be found .
The classifications of features involve the training of various emotional models to perform the classification appropriately . Another important aspect to be considered in emotional speech recognition is the database used for training the models [ 1 ] .
As for the benchmark , we use the frameworks of the second audio/visual emotion challenge and the Interspeech 2012 speaker trait challenge . Also proposed in this work is the use of part-of-speech tags as syntactic features of speech for emotional speech recognition . Results of this work show that despite the relatively small number of features that is used for the modeling tasks , generalization capability of the suggested models is comparable to those of other models that use thousands of features and more elaborate learning algorithms .
Emotional speech production has been previously studied using fleshpoint tracking data in speaker-specific experiment setups . The present study introduces a real-time magnetic resonance imaging database of emotional speech production from 10 speakers and presents articulatory analysis results of speech emotional expression using the database . Midsagittal vocal tract parameters ( midsagittal distances and the vocal tract length ) were parameterized based on a two-dimensional grid-line system , using image segmentation software .
To do this , we are trying to discuss what is essential in production and perception of non-linguistic information , to clarify biological common features among humans independent of languages , nations and cultures , and to apply these common features to man-machine communication as well as human-human communication . In this paper , as the results of our activities in perception of nonlinguistic information , we introduce a multi-layer emotional speech perception model and present some results of emotional speech synthesis and recognition using the model .
The 11 papers in this special issue can be categorized into five groups : Emotional speech synthesis and recognition ; affective video content analysis ; facial expressions and head movements ; affect analysis in small groups ; and audio-visual affective corpus .
This paper proposes an analysis method for emotional voice and application to emotional voice synthesis in man-machine interaction . The analysis is based on a high resolution time-frequency method and the clustering technique of four types of basic emotion .
The chapter introduces prosody conversion models for emotional speech generation by using a Gaussian Mixture Model ( GMM ) , and a Classification And Regression Tree ( CART ) model . Unlike the rule-based or linear modification method , the GMM and CART models try to map the subtle prosody distributions between neutral and emotional speech .
Moreover , and in spite of data availability , we also show how there are emotions ( sadness and anger ) that are clearly identified in the automatic experiments , for both languages , showing a reasonably clear " language-independent " behavior of such emotions . Finally , we show how psychological considerations related to the emotional speech generation process can be somehow related to our experimental results , suggesting that previously identifying such psychological features may greatly help the development of high quality " universal " emotional speech identification systems .
Advances in emotional speech recognition and synthesis essentially rely on the availability of annotated emotional speech corpora . As a low resource language , the Thai language critically lacks corpora of emotional speech , although a few corpora have been constructed for speech recognition and synthesis .
Experiments carried out on a French speech corpus show an improvement in the quality of speech synthesis when pronunciation models are included in the phonetization process . Appart from improving TTS quality , the presented pronunciation adaptation method also brings interesting perspectives in terms of expressive speech synthesis .
This paper analyzes and compares spectral properties ( first three formants position and spectral flatness measure values ) and prosodic parameters ( F0 and energy , microintonation and jitter ) of male and female acted emotional speech in Czech and Slovak languages . Statistical results and values of parameter ratios will be used for modification of the text-to-speech ( TTS ) system enabling expressive speech production with male / female voices , based on cepstral speech description .
The workshop is organized under the auspices of the SSPNet , the FP7 European Network of Excellence on Social Signal Processing ( EC FP7 grant agreement no. 231287 ) , and continues the tradition of the previous SSPW workshops by maintaining the high standard set by its predecessors . Main topics discussed during the SSPW workshop series include the following : Social Intelligence , Social Cognition and Social Behavior Modeling Facial behavior analysis and synthesis in social interactions Expressive speech analysis and synthesis in social interactions Human gesture and action recognition and synthesis in social interactions Multimodal human behavior analysis and synthesis in social interactions Perceptual , multimodal , and socially-aware user interfaces Socially-adept Embodied Conversational Agents Data Mining , Machine Learning , Information Retrieval , Artificial Intelligence in Social Contexts Databases for training and testing Socially-aware computing and applications ( reality mining , implicit multimedia tagging , etc . )
The solution is to synthesize realistic 3D talking faces by learning from facial motion capture data . This dissertation addresses three critical parts of realistic talking face synthesis : realistic eye motion synthesis , natural head motion synthesis , and expressive speech animation synthesis . A texture-synthesis based approach is presented to simultaneously synthesize realistic eye gaze and blink motion , accounting for any possible correlations between the two .
Modeling global expressiveness in speech synthesis . We present in this paper some modeling issues for expressive speech generation from a text necessarily determined by some extrinsic characteristics for natural output ( such as the textual genre , the reader 's profile and the discursive situation ) . We then argue for a five-level model for main speech expressiveness , which is in the very basis of symbolic coding , formal transformation and physical generation of some prosodic patterns one meets in today 's oral French .
Instead , the paper presents a personal account of some of the problems facing researchers who wish to study the speech characteristics associated with different emotions . It approaches the issue from the standpoint of speech technology , rather than that of psychology , and describes work planned under a forthcoming JST-funded five-year project for the study of ` expressive speech phenomena ’ which will include the production of a large-scale emotional-speech database . Rather than present new facts or data , the paper sets out some topics for discussion and raises questions ; in the hope that some of the issues may be resolved during the three days of the workshop .
Expressive Speech Recognition and Synthesis as Enabling Technologies for Affective Robot-Child Communication.- Embodied Conversational Agents : Computing and Rendering Realistic Gaze Patterns.- DBN Based Models for Audio-Visual Speech Analysis and Recognition.- An Extensive Method to Detect the Image Digital Watermarking Based on the Known Template.- Fast Mode Decision Algorithm in H.263+/H.264 Intra Transcoder.- Binary Erasure Codes for Packet Transmission Subject to Correlated Erasures.- Image Desynchronization for Secure Collusion-Resilient Fingerprint in Compression Domain.- A Format-Compliant Encryption Framework for JPEG2000 Image Code-Streams in Broadcasting Applications.- Euclidean Distance Transform of Digital Images in Arbitrary Dimensions.- JPEG2000 Steganography Possibly Secure Against Histogram-Based Attack.- Perceptual Depth Estimation from a Single 2D Image Based on Visual Perception Theory.- A System for Generating Personalized Virtual News.- Image Fingerprinting Scheme for Print-and-Capture Model.- 16x16 Integer Cosine Transform for HD Video Coding.- Heegard-Berger Video Coding Using LMMSE Estimator.- Real-Time BSD-Driven Adaptation Along the Temporal Axis of H.264/AVC Bitstreams.- Optimal Image Watermark Decoding.- Diagonal Discrete Cosine Transforms for Image Coding.- Synthesizing Variational Direction and Scale Texture on Planar Region.- Fast Content-Based Image Retrieval Based on Equal-Average K-Nearest-Neighbor Search Schemes.- Characterizing User Behavior to Improve Quality of Streaming Service over P2P Networks.- Interacting Activity Recognition Using Hierarchical Durational-State Dynamic Bayesian Network.- Improving the Image Retrieval Results Via Topic Coverage Graph.- Relevance Feedback for Sketch Retrieval Based on Linear Programming
This paper presents our recent and current work on expressive speech synthesis and recognition as enabling technologies for affective robot-child interaction . We show that current expression recognition systems could be used to discriminate between several archetypical emotions , but also that the old adage ” there ’s no data like more data ” is more than ever valid in this field .
Abstract Emotion is considered to be an essential element in the performance of human-computer interactions . In expressive synthesis speech , it is important to generate emotional speech that reflects subtle and complex emotional states . However , there has been limited research on how to effectively synthesize emotional speech using different levels of emotion strength with intuitive control , which is difficult to be modeled effectively .
This chapter contains sections titled : Emotion in Human Speech , Expressive Voice Synthesis , Implementation Overview , Kismet 's Expressive Utterances , Real-Time Lip Synchronization and Facial Animation , Limitations and Extensions , Summary
Ontology and parser dependencies approach for spoken dialogue system Vocal quality factors: analysis, synthesis, and perception. High Quality and Low Complexity Speech Analysis/Synthesis Based on Sinusoidal Representation Quality improvement of synthesized speech in noisy speech analysis‐synthesis processing Arabic speech synthesis and diacritic recognition Instruction-level performance modeling and characterization of multimedia applications A novel approach to speech signal synthesis Multi-Level Multi-Decision Model for Automatic Speech Recognition and Understanding New features in VTAR: A Matlab‐based computer program for vocal tract acoustic modeling Articulatory Speech Synthesis: A Survey Implementing Thai text-to-speech synthesis for hand-held devices Hybrid Text-to-Speech for Articulation Disorders with a Small Amount of Non-Parallel Data An Expanded Taxonomy of Semiotic Classes for Text Normalization Interacting with the Speech API Intelligent Conversational Bot for Massive Online Open Courses (MOOCs) Multiplicative LSTM for sequence modelling xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems An Actor-Critic Algorithm for Sequence Prediction Study on Emotional Speech Features in Korean with Its Application to Voice Conversion Human Emotion Recognition in Speech using Ant Colony Optimization Elastic net for paralinguistic speech recognition Vocal tract shaping of emotional speech Analysis of Production and Perception Characteristics of Non-linguistic Information in Speech and Its Application to Inter-language Communications Special Issue on Multimodal Affective Interaction Analysis and synthesis of emotional voice by time-frequency method Emotional Speech Generation by Using Statistic Prosody Conversion Methods In Search of Primary Rubrics for Language Independent Emotional Speech Identification Developing a Thai emotional speech corpus from Lakorn (EMOLA) Improving TTS with Corpus-Specific Pronunciation Adaptation Comparison of spectral and prosodic parameters of male and female emotional speech in Czech and Slovak Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding, J-HGBU@MM 2011, Scottsdale, Arizona, USA, December 1, 2011 Data-driven facial animation synthesis by learning from facial motion capture data What about the text? Modeling global expressiveness in speech synthesis DATABASES OF EMOTIONAL SPEECH Advances in Multimedia Information Processing - PCM 2006, 7th Pacific Rim Conference on Multimedia, Hangzhou, China, November 2-4, 2006, Proceedings Expressive Speech Recognition and Synthesis as Enabling Technologies for Affective Robot-Child Communication Building a controllable expressive speech synthesis system with multiple emotion strengths Expressive Vocalization System
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["Computer Science"] ["Physics","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Physics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Psychology"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Geography"] ["Computer Science"] ["Psychology","Computer Science"] ["Computer Science"]
2014 1991 2005 1981 2016 1999 2008 1999 2007 2011 2008 2018 2017 2017 2016 2017 2018 2017 2005 2016 2012 2020 2009 2010 2004 2009 2007 2019 2016 2011 2011 2006 2006 2000 2006 2006 2020 2004
ISSPIT CSE APSIPA INTERSPEECH ICLR KDD ICLR ACII ICMI Affective Information Processing INTERSPEECH ICASSP PCM
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