By using a generic communication framework , MARIE aims to create a flexible distributed component system that allows robotics developers to share software programs and algorithms , and design prototypes rapidly based on their own integration needs . The use of MARIE is illustrated with the design of a socially interactive autonomous mobile robot platform capable of map building , localization , navigation , tasks scheduling , sound source localization , tracking and separation , speech recognition and generation , visual tracking , message reading and graphical interaction using a touch screen interface . This paper proposes the development of a Numerical Command Recognition System of Speech Signal based on Neural Networks and DCT models . Thus , two configurations of neural networks , the Multilayer Perceptron and Learning Vector Quantization are evaluated by their performance in speech signal recognition , whose encoding is made by the mel-cepstral coefficients that are used to generate a two-dimensional time matrix by Discrete Cosine Transform ( DCT ) . The selection of the best configuration of neural network for classification of the patterns was carried out by comparative analysis of performance of the MLP and LVQ networks through training , validation and test of the network topology and learning algorithms previously established . This work proposes the analysis between two neural network configurations for development a intelligent recognition system of speech signal patterns of numerical commands in Brazilian Portuguese . Thus , the Multilayer Perceptron ( MLP ) and Learning Vector Quantization ( LVQ ) networks are evaluated their performance in the course of training , validation and testing in speech signal recognition , whose pattern of speech signal is given by a two-dimensional time matrix , resulting of the encoding of the mel-cepstral coefficients ( MFCC ) through application of discrete cosine transform ( DCT ) . These patterns have reduced set of parameters and the configurations of neural network in analysis use few examples for each pattern through training . Currently , speaker recognition system is an important need for authenticating the personal like other biometrics such as finger prints and retinal scans . Speech based recognition permits both on site and remote access to the user . In this research , speaker identification system is investigated from the speaker recognition problem point of view . We then analyze some gradient statistics , and we better study the impact of batch normalization . Moreover , we assess our approach on a larger variety of speech recognition tasks , considering several different datasets as well as noisy and reverberant conditions . Finally , we extend our experimental validation to a end - to - end CTC model . Sequence generation and prediction tasks span many modes of data , ranging from audio and language modelling , to more general timeseries prediction tasks . Applications of such models include speech recognition , machine translation , dialogue generation , speech synthesis , forecasting , and music generation , among others . Neural networks can be applied to these tasks by predicting sequence elements one - by - one , conditioning on the history of sequence elements , forming an autoregressive model . DBLP : conf / acl / SongPZWG17 and Speech Recognition zhang2017very . Most of the proposed Seq2Seq models can be viewed as a family of encoder - decoders DBLP : conf / nips / SutskeverVL14 , cho2014learning , bahdanau2014neural , where an encoder reads and encodes a source input in the form of sequences into a continuous vector representation of fixed dimension , and a decoder takes the encoded vectors and outputs a target sequence . A statistical language model estimates probability values P ( W ) for strings of words W in a vocabulary V whose size is in the tens , hundreds of thousands and sometimes even millions . Typically the string W is broken into sentences , or other segments such as utterances in automatic speech recognition , which are often assumed to be conditionally independent ; we will assume that W is such a segment , or sentence . Estimating full sentence language models is computationally hard if one seeks a properly normalized probability model 1 over strings of words of finite length in [ reference ] Previously , methods for estimating the performance of noisy speech recognition based on a spectral distortion measure have been proposed . Although they give an estimate of recognition performance without actually performing speech recognition , no consideration is given to any change in the components of a speech recognition system . We compare two different Kalman filtering approaches which attempt to improve noise robustness : Switching Linear Dynamic Models ( SLDM ) and Autoregressive Switching Linear Dynamical Systems ( AR-SLDS ) . Unlike previous works which are restricted on considering white noise , we evaluate the modeling concepts in a noisy speech recognition task where also colored noise produced through different driving conditions and car types is taken into account . Thereby we demonstrate that speech enhancement based on Kalman filtering prevails over all standard de-noising techniques considered herein , such as Wiener filtering , Histogram Equalization , and Unsupervised Spectral Subtraction . The trained DAE is used as a filter for speech restoration when noisy speech is given . Noisy robust speech recognition experiments were done to examine the performance of the trained deep network . Experimental results show that the DAE trained with pretraining process significantly improved the performance of speech restoration from noisy input . Performance of these features is tested on speeches randomly chosen from “ TI Digits_1 ” , “ TI Digits_2 ” and “ TIMIT ” databases . These perceptual features are evaluated with accuracy of 91 % for speaker independent isolated digit recognition , 99.5 % for clean continuous speech recognition and 87 % for noisy continuous speech recognition . In this work , F-ratio is computed as a theoretical measure to validate the experimental results and distribution is used to justify the good experimental results statistically . The paper concerns noisy speech recognition and understanding . It adopts the two level modular Extended Associative Memory Neural Networks ( EAMNN ) for speech recognition , information feedback of the linguistic constraint reasoning and statistical inference for understanding processing . An HMM-Based Mandarin Chinese Text-To-Speech System.- HMM-Based Emotional Speech Synthesis Using Average Emotion Model.- A Hakka Text-To-Speech System.- Speech Enhancement.- Adaptive Null-Forming Algorithm with Auditory Sub-bands.- Multi-channel Noise Reduction in Noisy Environments.- Acoustic Modeling for Automatic Speech Recognition.- Minimum Phone Error ( MPE ) Model and Feature Training on Mandarin Broadcast News Task.- State-Dependent Phoneme-Based Model Merging for Dialectal Chinese Speech Recognition.- Non-uniform Kernel Allocation Based Parsimonious HMM.- Consistent Modeling of the Static and Time-Derivative Cepstrums for Speech Recognition Using HSPTM.- Robust Speech Recognition.- Vector Autoregressive Model for Missing Feature Reconstruction.- Auditory Contrast Spectrum for Robust Speech Recognition.- Signal Trajectory Based Noise Compensation for Robust Speech Recognition.- An HMM Compensation Approach Using Unscented Transformation for Noisy Speech Recognition.- Noisy Speech Recognition Performance of Discriminative HMMs.- Distributed Speech Recognition of Mandarin Digits String.- Speech Adaptation/Normalization.- Unsupervised Speaker Adaptation Using Reference Speaker Weighting.- Automatic Construction of Regression Class Tree for MLLR Via Model-Based Hierarchical Clustering.- General Topics in Speech Recognition.- A Minimum Boundary Error Framework for Automatic Phonetic Segmentation.- Large Vocabulary Continuous Speech Recognition.- Advances in Mandarin Broadcast Speech Transcription at IBM Under the DARPA GALE Program.- Improved Large Vocabulary Continuous Chinese Speech Recognition by Character-Based Consensus Networks.- All-Path Decoding Algorithm for Segmental Based Speech Recognition.- Improved Mandarin Speech Recognition by Lattice Rescoring with Enhanced Tone Models.- On Using Entropy Information to Improve Posterior Probability-Based Confidence Measures.- Vietnamese Automatic Speech Recognition : The FLaVoR Approach.- Multilingual Recognition and Identification.- Language Identification by Using Syllable-Based Duration Classification on Code-Switching Speech.- Speaker Recognition and Characterization.- CCC Speaker Recognition Evaluation 2006 : Overview , Methods , Data , Results and Perspective.- The RAFF is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference , and may be considered to be constructed from a series of dynamic fuzzy rules . As compared to other existing nonlinear filters , three major advantages of the RAFF are observed : 1 ) a priori knowledge can be incorporated into the RAFF , which makes the fusion of numerical data and linguistic information possible ; 2 ) owing to the dynamic property of the RAFF , the exact lagged order of the input variables need not be known in advance ; 3 ) no predetermination , like the number of hidden nodes , must be given since the RAFF can find its optimal structure and parameters automatically Several examples on adaptive noise cancellation and noisy speech recognition problems using the RAFF are illustrated to demonstrate the performance of the RAFF . For this improvement , developments of the standard evaluation corpus and assessment technologies are essential . Recently the AURORA-2,3 corpus and their evaluation scenarios have had significant impacts on noisy speech recognition research . This paper introduces a Japanese noisy speech corpus and its evaluation scripts , called AURORA-2J . To improve the speech quality and enhance the performance of noisy speech signals recognition , a new speech enhancement algorithm based on improved spectral subtraction is proposed and analyzed . In contrast to the standard spectral subtraction algorithm , the new algorithm accurately estimates the noise based on the fact that all noise can be changed into Additive White Gaussian Noise(AWGN ) and according to the feature that the amplitude spectral of narrowband white Gaussian noise obeys Rayleigh distribution . Because this system learns directly from data , we do not require specialized components for speaker adaptation or noise filtering . In fact , in settings where robustness to speaker variation and noise are critical , our system excels : Deep Speech outperforms previously published methods on the Switchboard Hub5’00 corpus , achieving 16.0 % error , and performs better than commercial systems in noisy speech recognition tests . Traditional speech systems use many heavily engineered processing stages , including specialized input features , acoustic models , and Hidden Markov Models ( HMMs ) . The following speech‐processing systems are described and demonstrated : ( 1 ) phonetic typewriter — the conversion of speech to the typed page ; ( 2 ) speech analysis , conversion to a code , transmission of the coded signal , and the synthesis of speech from the code ; ( 3 ) application of language translation of speech to the typed page and to synthesized speech . For the purpose of comparing the different unwrapping methods used for quadratic interpolation , the four unwrapping methods are used to synthesize voiced speech using the MBE model . Both the theoretical analysis and simulation results show that the fourth phase unwrapping method outperforms the other methods for voiced speech synthesis . This short paper demonstrates an application of Gaussian processes to voiced speech synthesis . The novelty lies in the application of an auxiliary input , which acts to vary the pitch of the synthesised sound . An experimental software system has been developed to perform syllable-based speech synthesis . The synthesis units are demisyllables with affixes , whereby the demisyllables consist of a vowel portion and a consonant cluster . By incorporating dynamic information within the parameterisation of lip movements we can model the cyclical structure , as well as the causal nature of speech movements as described by an underlying visual speech manifold . It is believed that such a structure will be appropriate to various areas of speech modeling , in particular the synthesis of speech lip movements . By incorporating dynamic information within the parameterisation of lip movements we can model the cyclical structure , as well as the causal nature of speech movements as described by an underlying visual speech manifold . It is believed that such a structure will be appropriate to various areas of speech modeling , in particular the synthesis of speech lip movements . The purpose of OTOCKER is to act as a voice data collection platform for intercultural communication . Although speech synthesis systems have improved significantly , fluent and smooth speech synthesis is still a problem . Currently , it is difficult to synthesize speech in different languages . Preliminary experiments of synthesizing several phones including both vowels and consonants showed a potential of the technique . In this paper , the previous work is extended to full-sentence speech synthesis using sparse GPs and context modification . Specifically , clusterbased sparse Gaussian processes such as local GPs and partially independent conditional ( PIC ) approximation are examined as a computationally feasible approach . Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise . In this work , we explore the task of lip to speech synthesis , i.e. , learning to generate natural speech given only the lip movements of a speaker . Acknowledging the importance of contextual and speaker-specific cues for accurate lip-reading , we take a different path from existing works . In this paper , an efficient human face detection , recognition and Text To Speech ( TTS ) based wishing system is designed . The system design involves face detection using Viola-jones object detection algorithm , train the data base by finding the speeded up robust features (SURF)[9 ] features of detected faces , match of test features with train database features using Fast library for approximate Nearest Neighbors(FLANN ) based matching technique , and finally announcing recognized human name based on training using text to speech based speech synthesis . The main problem in this type of systems design is the test face may have difference in view point or scale or illumination , when compared with trained faces , these factors will affect the recognition accuracy . A method of speech driven lip synthesis which applies viseme based training models to units of visual speech . The audio data is grouped into a smaller number of visually distinct visemes rather than the larger number of phonemes . This paper presents a global lip texture description based on principle component analysis for speech synchronous lip movement synthesize system based on Hidden Markov Models(HMM).And with this basement , K harmonic means which isn′t sensitive for the initialization is used for clustering 1650 pieces of 24 bit lip images in the research , in order to establish mapping relative between speech features and lip features during speech lip synthesizing research . The experiment indicates that the ideas presented in this paper not only keep lip texture during lip movement , but also avoid the bad influence in lip synthesizing because of the feature points unfaithfully extracting .
By using a generic communication framework , MARIE aims to create a flexible distributed component system that allows robotics developers to share software programs and algorithms , and design prototypes rapidly based on their own integration needs . The use of MARIE is illustrated with the design of a socially interactive autonomous mobile robot platform capable of map building , localization , navigation , tasks scheduling , sound source localization , tracking and separation , speech recognition and generation , visual tracking , message reading and graphical interaction using a touch screen interface .
This paper proposes the development of a Numerical Command Recognition System of Speech Signal based on Neural Networks and DCT models . Thus , two configurations of neural networks , the Multilayer Perceptron and Learning Vector Quantization are evaluated by their performance in speech signal recognition , whose encoding is made by the mel-cepstral coefficients that are used to generate a two-dimensional time matrix by Discrete Cosine Transform ( DCT ) . The selection of the best configuration of neural network for classification of the patterns was carried out by comparative analysis of performance of the MLP and LVQ networks through training , validation and test of the network topology and learning algorithms previously established .
This work proposes the analysis between two neural network configurations for development a intelligent recognition system of speech signal patterns of numerical commands in Brazilian Portuguese . Thus , the Multilayer Perceptron ( MLP ) and Learning Vector Quantization ( LVQ ) networks are evaluated their performance in the course of training , validation and testing in speech signal recognition , whose pattern of speech signal is given by a two-dimensional time matrix , resulting of the encoding of the mel-cepstral coefficients ( MFCC ) through application of discrete cosine transform ( DCT ) . These patterns have reduced set of parameters and the configurations of neural network in analysis use few examples for each pattern through training .
Currently , speaker recognition system is an important need for authenticating the personal like other biometrics such as finger prints and retinal scans . Speech based recognition permits both on site and remote access to the user . In this research , speaker identification system is investigated from the speaker recognition problem point of view .
We then analyze some gradient statistics , and we better study the impact of batch normalization . Moreover , we assess our approach on a larger variety of speech recognition tasks , considering several different datasets as well as noisy and reverberant conditions . Finally , we extend our experimental validation to a end - to - end CTC model .
Sequence generation and prediction tasks span many modes of data , ranging from audio and language modelling , to more general timeseries prediction tasks . Applications of such models include speech recognition , machine translation , dialogue generation , speech synthesis , forecasting , and music generation , among others . Neural networks can be applied to these tasks by predicting sequence elements one - by - one , conditioning on the history of sequence elements , forming an autoregressive model .
DBLP : conf / acl / SongPZWG17 and Speech Recognition zhang2017very . Most of the proposed Seq2Seq models can be viewed as a family of encoder - decoders DBLP : conf / nips / SutskeverVL14 , cho2014learning , bahdanau2014neural , where an encoder reads and encodes a source input in the form of sequences into a continuous vector representation of fixed dimension , and a decoder takes the encoded vectors and outputs a target sequence .
A statistical language model estimates probability values P ( W ) for strings of words W in a vocabulary V whose size is in the tens , hundreds of thousands and sometimes even millions . Typically the string W is broken into sentences , or other segments such as utterances in automatic speech recognition , which are often assumed to be conditionally independent ; we will assume that W is such a segment , or sentence . Estimating full sentence language models is computationally hard if one seeks a properly normalized probability model 1 over strings of words of finite length in [ reference ]
Previously , methods for estimating the performance of noisy speech recognition based on a spectral distortion measure have been proposed . Although they give an estimate of recognition performance without actually performing speech recognition , no consideration is given to any change in the components of a speech recognition system .
We compare two different Kalman filtering approaches which attempt to improve noise robustness : Switching Linear Dynamic Models ( SLDM ) and Autoregressive Switching Linear Dynamical Systems ( AR-SLDS ) . Unlike previous works which are restricted on considering white noise , we evaluate the modeling concepts in a noisy speech recognition task where also colored noise produced through different driving conditions and car types is taken into account . Thereby we demonstrate that speech enhancement based on Kalman filtering prevails over all standard de-noising techniques considered herein , such as Wiener filtering , Histogram Equalization , and Unsupervised Spectral Subtraction .
The trained DAE is used as a filter for speech restoration when noisy speech is given . Noisy robust speech recognition experiments were done to examine the performance of the trained deep network . Experimental results show that the DAE trained with pretraining process significantly improved the performance of speech restoration from noisy input .
Performance of these features is tested on speeches randomly chosen from “ TI Digits_1 ” , “ TI Digits_2 ” and “ TIMIT ” databases . These perceptual features are evaluated with accuracy of 91 % for speaker independent isolated digit recognition , 99.5 % for clean continuous speech recognition and 87 % for noisy continuous speech recognition . In this work , F-ratio is computed as a theoretical measure to validate the experimental results and distribution is used to justify the good experimental results statistically .
The paper concerns noisy speech recognition and understanding . It adopts the two level modular Extended Associative Memory Neural Networks ( EAMNN ) for speech recognition , information feedback of the linguistic constraint reasoning and statistical inference for understanding processing .
An HMM-Based Mandarin Chinese Text-To-Speech System.- HMM-Based Emotional Speech Synthesis Using Average Emotion Model.- A Hakka Text-To-Speech System.- Speech Enhancement.- Adaptive Null-Forming Algorithm with Auditory Sub-bands.- Multi-channel Noise Reduction in Noisy Environments.- Acoustic Modeling for Automatic Speech Recognition.- Minimum Phone Error ( MPE ) Model and Feature Training on Mandarin Broadcast News Task.- State-Dependent Phoneme-Based Model Merging for Dialectal Chinese Speech Recognition.- Non-uniform Kernel Allocation Based Parsimonious HMM.- Consistent Modeling of the Static and Time-Derivative Cepstrums for Speech Recognition Using HSPTM.- Robust Speech Recognition.- Vector Autoregressive Model for Missing Feature Reconstruction.- Auditory Contrast Spectrum for Robust Speech Recognition.- Signal Trajectory Based Noise Compensation for Robust Speech Recognition.- An HMM Compensation Approach Using Unscented Transformation for Noisy Speech Recognition.- Noisy Speech Recognition Performance of Discriminative HMMs.- Distributed Speech Recognition of Mandarin Digits String.- Speech Adaptation/Normalization.- Unsupervised Speaker Adaptation Using Reference Speaker Weighting.- Automatic Construction of Regression Class Tree for MLLR Via Model-Based Hierarchical Clustering.- General Topics in Speech Recognition.- A Minimum Boundary Error Framework for Automatic Phonetic Segmentation.- Large Vocabulary Continuous Speech Recognition.- Advances in Mandarin Broadcast Speech Transcription at IBM Under the DARPA GALE Program.- Improved Large Vocabulary Continuous Chinese Speech Recognition by Character-Based Consensus Networks.- All-Path Decoding Algorithm for Segmental Based Speech Recognition.- Improved Mandarin Speech Recognition by Lattice Rescoring with Enhanced Tone Models.- On Using Entropy Information to Improve Posterior Probability-Based Confidence Measures.- Vietnamese Automatic Speech Recognition : The FLaVoR Approach.- Multilingual Recognition and Identification.- Language Identification by Using Syllable-Based Duration Classification on Code-Switching Speech.- Speaker Recognition and Characterization.- CCC Speaker Recognition Evaluation 2006 : Overview , Methods , Data , Results and Perspective.-
The RAFF is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference , and may be considered to be constructed from a series of dynamic fuzzy rules . As compared to other existing nonlinear filters , three major advantages of the RAFF are observed : 1 ) a priori knowledge can be incorporated into the RAFF , which makes the fusion of numerical data and linguistic information possible ; 2 ) owing to the dynamic property of the RAFF , the exact lagged order of the input variables need not be known in advance ; 3 ) no predetermination , like the number of hidden nodes , must be given since the RAFF can find its optimal structure and parameters automatically Several examples on adaptive noise cancellation and noisy speech recognition problems using the RAFF are illustrated to demonstrate the performance of the RAFF .
For this improvement , developments of the standard evaluation corpus and assessment technologies are essential . Recently the AURORA-2,3 corpus and their evaluation scenarios have had significant impacts on noisy speech recognition research . This paper introduces a Japanese noisy speech corpus and its evaluation scripts , called AURORA-2J .
To improve the speech quality and enhance the performance of noisy speech signals recognition , a new speech enhancement algorithm based on improved spectral subtraction is proposed and analyzed . In contrast to the standard spectral subtraction algorithm , the new algorithm accurately estimates the noise based on the fact that all noise can be changed into Additive White Gaussian Noise(AWGN ) and according to the feature that the amplitude spectral of narrowband white Gaussian noise obeys Rayleigh distribution .
Because this system learns directly from data , we do not require specialized components for speaker adaptation or noise filtering . In fact , in settings where robustness to speaker variation and noise are critical , our system excels : Deep Speech outperforms previously published methods on the Switchboard Hub5’00 corpus , achieving 16.0 % error , and performs better than commercial systems in noisy speech recognition tests . Traditional speech systems use many heavily engineered processing stages , including specialized input features , acoustic models , and Hidden Markov Models ( HMMs ) .
The following speech‐processing systems are described and demonstrated : ( 1 ) phonetic typewriter — the conversion of speech to the typed page ; ( 2 ) speech analysis , conversion to a code , transmission of the coded signal , and the synthesis of speech from the code ; ( 3 ) application of language translation of speech to the typed page and to synthesized speech .
For the purpose of comparing the different unwrapping methods used for quadratic interpolation , the four unwrapping methods are used to synthesize voiced speech using the MBE model . Both the theoretical analysis and simulation results show that the fourth phase unwrapping method outperforms the other methods for voiced speech synthesis .
This short paper demonstrates an application of Gaussian processes to voiced speech synthesis . The novelty lies in the application of an auxiliary input , which acts to vary the pitch of the synthesised sound .
An experimental software system has been developed to perform syllable-based speech synthesis . The synthesis units are demisyllables with affixes , whereby the demisyllables consist of a vowel portion and a consonant cluster .
By incorporating dynamic information within the parameterisation of lip movements we can model the cyclical structure , as well as the causal nature of speech movements as described by an underlying visual speech manifold . It is believed that such a structure will be appropriate to various areas of speech modeling , in particular the synthesis of speech lip movements .
By incorporating dynamic information within the parameterisation of lip movements we can model the cyclical structure , as well as the causal nature of speech movements as described by an underlying visual speech manifold . It is believed that such a structure will be appropriate to various areas of speech modeling , in particular the synthesis of speech lip movements .
The purpose of OTOCKER is to act as a voice data collection platform for intercultural communication . Although speech synthesis systems have improved significantly , fluent and smooth speech synthesis is still a problem . Currently , it is difficult to synthesize speech in different languages .
Preliminary experiments of synthesizing several phones including both vowels and consonants showed a potential of the technique . In this paper , the previous work is extended to full-sentence speech synthesis using sparse GPs and context modification . Specifically , clusterbased sparse Gaussian processes such as local GPs and partially independent conditional ( PIC ) approximation are examined as a computationally feasible approach .
Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise . In this work , we explore the task of lip to speech synthesis , i.e. , learning to generate natural speech given only the lip movements of a speaker . Acknowledging the importance of contextual and speaker-specific cues for accurate lip-reading , we take a different path from existing works .
In this paper , an efficient human face detection , recognition and Text To Speech ( TTS ) based wishing system is designed . The system design involves face detection using Viola-jones object detection algorithm , train the data base by finding the speeded up robust features (SURF)[9 ] features of detected faces , match of test features with train database features using Fast library for approximate Nearest Neighbors(FLANN ) based matching technique , and finally announcing recognized human name based on training using text to speech based speech synthesis . The main problem in this type of systems design is the test face may have difference in view point or scale or illumination , when compared with trained faces , these factors will affect the recognition accuracy .
A method of speech driven lip synthesis which applies viseme based training models to units of visual speech . The audio data is grouped into a smaller number of visually distinct visemes rather than the larger number of phonemes .
This paper presents a global lip texture description based on principle component analysis for speech synchronous lip movement synthesize system based on Hidden Markov Models(HMM).And with this basement , K harmonic means which isn′t sensitive for the initialization is used for clustering 1650 pieces of 24 bit lip images in the research , in order to establish mapping relative between speech features and lip features during speech lip synthesizing research . The experiment indicates that the ideas presented in this paper not only keep lip texture during lip movement , but also avoid the bad influence in lip synthesizing because of the feature points unfaithfully extracting .
Robotic Software Integration Using MARIE Intelligent system of speech recognition using Neural Networks based on DCT parametric models of low order Neural Network Configurations Analysis for Identification of Speech Pattern with Low Order Parameters A Word-Dependent Automatic Arabic Speaker Identification System Light Gated Recurrent Units for Speech Recognition Dynamic Evaluation of Neural Sequence Models Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation Performance estimation of noisy speech recognition using spectral distortion and recognition task complexity Switching Linear Dynamic Models for Noise Robust In-Car Speech Recognition Speech restoration based on deep learning autoencoder with layer-wised pretraining Perceptual Features Based Isolated Digit and Continuous Speech Recognition Using Iterative Clustering Approach Neural network noisy speech recognition and understanding with information feedback Chinese Spoken Language Processing, 5th International Symposium, ISCSLP 2006, Singapore, December 13-16, 2006, Proceedings Noisy speech processing by recurrently adaptive fuzzy filters DATA COLLECTION AND EVALUATION OF AURORA-2 JAPANESE CORPUS Speech enhancement algorithm based on improved spectral subtraction Deep Speech: Scaling up end-to-end speech recognition Speech‐Processing Systems Phase unwrapping methods for quadratic phase interpolation in voiced speech sythesis Using Gaussian processes to synthesise voiced speech with natural pitch variations An approach to demisyllable speech synthesis of German words Visual speech synthesis from 3D video Parameterisation of 3d speech lip movements A Web-based Multilingual Utterance Collection System for the Medical Field Statistical nonparametric speech synthesis using sparse Gaussian processes Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis Design of human face detection and recognition system along with speech synthesis subtitle Speech driven lip synthesis using viseme based hidden markov models Lip Feature Extracting Research Based on PCA and KHM Clustering Algorithm
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["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering"] ["Computer Science"] ["Physics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Physics"] ["Computer Science"]
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