Between speech training and speech recognition , we provide speech teachers with an innovative tool for teaching deaf children how to master the articulation of voiced sounds . Two theoretical aspects of this work are important : \bulletPhonemes are seen as elements of an organized structure\bulletThe phonetic recognition is based on rejection principles The following developments derive from those two main ideas:\bulletIn a set of N phonemes , a phoneme is defined by N - 1 relations which the N - 1 possible pairs determine.\bulletIn the recognition phase , when confronting two phonemes , the refutation discards the least probable one . The speech recognition rate of this method reach ninety-one percents accurately under no noise environment . Speech signal recognition is feasible and effective in noisy environment . This work introduces a Brazilian Speech Database ( BrSD ) , a novel dataset freely available created to support the development of speech-based recognition tasks . As far as we know , this is the first Portuguese language based database with these characteristics created and made available to the research community . We find several interesting interactions which suggest that the discourse structure can play an important role in several dialogue related tasks : automatic detection of SRP and analyzing spoken dialogues systems with a large state space from limited amounts of available data . Index Terms : discourse structure , speech recognition analysis , spoken dialogue systems . This approach contrasts sharply with the typical method of creating such a dictionary by human experts , which can be a time-consuming and expensive endeavor . Our experiments show that automatically derived lexicons allow us to build speech recognizers that consistently perform closely to supervised speech recognizers , which should enable more rapid development of speech recognition capability for low-resource languages . section : Introduction Over the last years , thanks in part to steady improvements brought by deep learning approaches to speech recognition , voice interfaces have greatly evolved from spotting limited and predetermined keywords to understanding arbitrary formulations of a given intention . They also became much more reliable , with state - of - the - art speech recognition engines reaching human level in English . Recurrent neural networks ( RNNs ) , such as long short - term memory networks ( LSTMs ) hochreiter1997long , form an important building block for many tasks that require modeling of sequential data . RNNs have been successfully employed for several such tasks including language modeling melis2017state , merity2017regularizing , speech recognition xiong2017microsoft , graves2013speech , and machine translation wu2016google , bahdanau2014neural . Computational costs and numerical instability led to these techniques to be abandoned for almost a decade . Recently , distributed computing allowed for larger models to be trained [ reference ] , and the growth of data for unsupervised learning algorithms to emerge [ reference ] . Distributed representations usually are trained through neural networks , these networks have made advancements in diverse fields such as computer vision [ reference ] , speech recognition [ reference ] , and natural language processing [ reference ] . section : CONCLUSIONS Also , compared to concatenation , our method does not significantly increase the number of parameters . Highway LSTMs and depth - gated LSTMs are similar to our proposed models in that they use cell states from the previous layer , and they are successfully applied to the field of automatic speech recognition and language modeling . However in contrast to CAS - LSTM , where the additional forget gate aggregates the previous layer states , and thus contexts from the left and below participate in computation equitably , in Highway LSTMs and depth - gated LSTMs the previous layer states are considered only through peephole connections . TitouanParcollet , YingZhang , MohamedMorchid , ChihebTrabelsi , GeorgesLinarès , RenatoDeMori andYoshuaBengio Universitéd’Avignon , LIA , France UniversitédeMontréal , MILA , Canada McGillUniversity , Montréal , Canada Orkis , Aixenprovence , France ElementAI , Montréal , Canada titouan.parcollet@alumni.univ - avignon.fr , ying.zhlisa@gmail.com , mohamed.morchid@univ - avignon.fr , chiheb.trabelsi@polymtl.ca , georges.linares@univ - avignon.fr , rdemori@cs.mcgill.ca Index Terms : quaternion convolutional neural networks , automatic speech recognition , deep learning section : Introduction section : Introduction Recurrent neural networks ( RNNs ) like long short - term memory ( LSTM ; ) networks and gated recurrent unit ( GRU ; ) are popular architectures for sequence modeling tasks like language generation , translation , speech synthesis , and machine comprehension . However , they are harder to optimize compared to feed - forward networks due to challenges like variable length input sequences , repeated application of the same transition operator at each time step , and largely - dense embedding matrix that depends on the vocabulary size . Further work is needed to tackle this from of instability . We think that extending this framework to other domains such as video ( for frame prediction ) and audio ( pre - trained features for speech synthesis ) should be very interesting . Further investigations into the properties of the learnt latent space would be interesting as well . In our settings , the input sequences for the th biLSTM layer are the concatenated outputs of all the previous layers , plus the original word embedding sequence . This gives a shortcut connection style setup , related to the widely used idea of residual connections in CNNs for computer vision , highway networks for RNNs in speech processing , and shortcut connections in hierarchical multitasking learning ; but in our case we feed in all the previous layers ’ output sequences as well as the word embedding sequence to every layer . Let represent words in the source sentence . Since our proposed method can be used independently for noise reduction , it can be combined with the traditional noise reduction methods to further reduce the noise effect . We tested our proposed method as a front-end for robust speech recognition on the AURORA-2J data corpus . Two advanced noise reduction methods , ETSI advanced front-end ( AFE ) method , and particle filtering ( PF ) with minimum mean square error ( MMSE ) estimation method , are used for comparison and combinations . This paper proposes a speech feature extraction method that utilizes periodicity and nonperiodicity for robust automatic speech recognition . The method was motivated by the auditory comb filtering hypothesis proposed in speech perception research . An unavoidable problem in speech technology , particularly in the development of robust automatic speech recognition systems , is the extreme variability in the acoustic attributes of segments . Segments are highly sensitive to context and bear little resemblance to their intrinsic characteristics manifested when they are uttered in isolation . This paper addresses the robust speech recognition problem as an adaptation task . Specifically , we investigate the cumulative application of adaptation methods . Speech feature extraction has been a key focus in robust speech recognition research . Selecting proper features is the key of effective system performance . The tracker incorporates predictive dynamics which can be learned from training sequences and automatically tuned to follow typical motions found in speech . The visual data from the tracker is incorporated into an acoustic automatic speech recogniser enabling robust recognition of speech in the presence of acoustic noise . Tests on a 40 word vocabulary using a dynamic time warping based audio-visual recogniser demonstrate that the lip outline is a rich source of information for speech recognition and establish dynamic contour tracking as a viable instrument for near real-time speechreading applications . This paper studies the use of hybrid context-dependent Deep Neural Network Hidden Markov Model ( DNN-HMM ) architecture for robust recognition of speech affected by realworld nonlinear distortions . We consider two types of distortions ; a ) signals distorted through overgained microphone preamplifier in the analog domain and b ) recordings exhibiting unnatural spectral sparseness , caused by excessive denoising or low-bit-rate compression . In this paper , we study a class of robust automatic speech recognition problem in which mismatches between training and testing conditions exist but an accurate knowledge of the mismatch mechanism is unknown . The only available information is the test data along with a set of pretrained speech models and the decision parameters . One approach to this problem is to achieve the desired robustness in speech representations used in the ASR . Motivated from studies on robust human speech recognition , we analyse the unsupervised data-driven temporal modulation filter learning for robust feature extraction . In this paper , we compare various unsupervised models for data driven filter learning like convolutional autoencoder ( CAE ) , generative adversarial network ( GAN ) and convolutional restricted Boltzmann machine ( CRBM ) . Accurate and effective voice activity detection ( VAD ) is a fundamental step for robust speech or speaker recognition . In this study , we proposed a hierarchical framework approach for VAD and speech enhancement . EVERA is written in an object-oriented framework and provides a high-level graphic user interface to design , observe and to monitor different simulations . We present some applications of the system to the problem of robust speech perception and recognition in hostile synthetic acoustic environments built from some physical model of real rooms ( 4 walls , 1 floor and 1 ceiling ) which include different real world noises . Experiments show that the proposed dynamic evaluation methodology gives large test time improvements across character and word level language modelling . Our improvements to language modelling have applications to speech recognition and machine translation over longer contexts , including broadcast speech recognition and paragraph level machine translation . Overall , dynamic evaluation is shown to be an effective method for exploiting pattern re - occurrence in sequences . In this paper , we describe an end - to - end speech system , called “ Deep Speech ” , where deep learning supersedes these processing stages . Combined with a language model , this approach achieves higher performance than traditional methods on hard speech recognition tasks while also being much simpler . These results are made possible by training a large recurrent neural network ( RNN ) using multiple GPUs and thousands of hours of data . Finally , we introduce multi - scale input features aimed at exploiting more context at negligible computational cost . We evaluate the improvements first on a Babel task for low resource speech recognition , obtaining an absolute 5.77 % WER improvement over the baseline PLP DNN by training our CNN on the combined data of six different languages . We then evaluate the very deep CNNs on the Hub5’00 benchmark ( using the 262 hours of SWB - 1 training data ) achieving a word error rate of 11.8 % after cross - entropy training , a 1.4 % WER improvement ( 10.6 % relative ) over the best published CNN result so far . This paper addresses the problem of distant speech recognition in reverberant noisy conditions employing a star-shaped microphone array and vector Taylor series VTS compensation . First , a beamformer yields an enhanced single-channel signal by applying convex CVX optimization over three spatial dimensions given the spatio-temporal position of the target speaker as prior knowledge . This paper presents a new microphone-array post-filtering algorithm for distant speech recognition ( DSR ) . Conventionally , post-filtering methods assume static noise field models , and using this assumption , employ a Wiener filter mechanism for estimating the noise parameters . Room reverberation and environment noise pose significant challenges for both speech enhancement and distant speech recognition ( DSR ) . However , the rapid progresses achieved recently in the two fields are somehow ignored by each other . We believe that “ only good signal processing can lead to top ASR performance ” in challenging acoustic environments . This notion leads to a unified deep neural network ( DNN ) framework for distant speech processing that can achieve both high-quality enhanced speech and high-accuracy ASR simultaneously . Our goal is accomplished by two techniques , namely : ( i ) a reverberation-time-aware DNN based speech dereverberation architecture that can handle a wide range of reverberation times to enhance speech quality of reverberant and noisy speech , followed by ( ii ) DNN-based multicondition training that takes both clean-condition and multicondition speech into consideration , leveraging upon an exploitation of the data acquired and processed with multichannel microphone arrays , to improve ASR performance . In this paper , we describe the REVERB challenge , which is an evaluation campaign that was designed to evaluate such speech enhancement ( SE ) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions . Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single-channel scenario , a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation , based on both real , single-channel , and multichannel recordings . This challenge attracted 27 papers , which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes . The robustness of this speaker recognition method for the change of the speaking style in close-talking environment was evaluated in [ 2 ] . We integrated this method to the proposed position dependent CMN for distant speaker recognition . Our experiments showed that the proposed method improved the speaker recognition performance remarkably in a distant environment . For close-talking speaker recognition , a speaker recognition method by combining speaker-specific GMMs with speaker-adapted syllable-based HMMs has been proposed , which shows robustness for the change of the speaking style in a close-talking environment . In this thesis , we extend this combination method to distant speaker recognition and integrate this method with the proposed position-dependent CMN . Speaker recognition experiments were conducted on NTT database ( 22 male speakers ) and Tohoku University and Panasonic database ( 20 male speakers ) . This paper presents new superdirective beamforming algorithms based on the maximum negentropy ( MN ) criterion for distant automatic speech recognition . The MN beamformer is configured in the generalized sidelobe canceler structure , and uses the weights derived from a delay-and-sum beamformer as the quiescent weight vector . Microphone array beamforming has been approved to be an effective approach for suppressing adverse interferences . Recently , acoustic beamformers employing neural networks ( NN ) for time-frequency ( T-F ) mask prediction , termed as Mask-BF , have received tremendous interest and shown great benefits as a front-end for distant automatic speech recognition ( ASR ) . However , our preliminary experiments using Mask-BF for ASR task show that the mask model trained with only simulated training data underperforms when the real-recording data appears in the testing stage , where a data mismatch problem occurs . Although state of the art speech processing has achieved great performance for most widely used languages , little to no efforts were made for under-resourced languages , such as Romanian . This paper focuses on a set of experiments in building a series of acoustic and grammar models for Romanian language , to be used in distant speech recognition scenarios , for voice driven speech applications in intelligent homes or buildings , using previously acquired speech databases in Romanian language , in real life conditions , by our research group . The problem of distant recording and recognition of speech for the task of voice interaction with automatic inquiry system in noisy environment is considered . The system perceives and analyses sounds arising from limited subspace due to spatial localization of sound sources . This paper focuses on the methodology of acquiring four different speech corpora with various end-user scenarios in mind . The commands corpus is meant to be used in home automation development , the cough corpus is meant to help research in detecting distress situations , the spontaneous speech corpus will aid in distant speech recognition applications and the multi-room , multi-person , multi-language corpus can be used for research in speaker detection and identification . All these were recorded in the context of a completely automated and functional smart home . Second , the proposed recursive architecture in the BridgeNet can iteratively improve denoising and recognition performance . The experimental results of BridgeNet showed significant improvements in tackling the distant speech recognition problem , where it achieved up to 13.24 % relative WER reductions on AMI corpus compared to a baseline neural network without teacher 's hints . However , as no common reverberation criteria for the recognition of reverberant speech have yet been proposed , it has been difficult to estimate its effectiveness . To address this problem in 2007 , we investigated early and late reflections on distanttalking speech recognition to help define suitable reverberation criteria . We here propose new reverberation criteria RSR-Dn ( Reverberant Speech Recognition criteria with Dn ) based on our previous investigations and ISO3382 acoustic parameters . The author takes the view that improvements in performance will continue to be based on both technological and linguistic knowledge . To illustrate this , the paper reviews ongoing work aimed at overcoming the deficiencies of the current paradigm and elaborates research on speaker adaptation and accented speech recognition . This work offers considerable potential to improve future systems ' performance . Various techniques , such as acoustic model adaptation and pronunciation adaptation , have been reported to improve the recognition of non-native or accented speech . In this paper , we propose a new approach that combines accent detection , accent discriminative acoustic features , acoustic adaptation and model selection for accented Chinese speech recognition . Experimental results show that this approach can improve the recognition of accented speech . This parameter is used to adjust the HMM state densities for each input speech frame , leading to both high robustness and good resolution for dynamic tracking to cover a diversity of temporal variations in speech . Tested on an accented speech recognition application , the proposed framework yields an improved syllable error rate reduction over the conventional DGMS and augmented HMM systems when evaluated on three typical Chinese accents , Chuan , Yue and Wu , while maintaining its performance for standard Putonghua . In this paper , we propose a domain adversarial training ( DAT ) algorithm to alleviate the accented speech recognition problem . In order to reduce the mismatch between labeled source domain data ( “ standard ” accent ) and unlabeled target domain data ( with heavy accents ) , we augment the learning objective for a Kaldi TDNN network with a domain adversarial training ( DAT ) objective to encourage the model to learn accent-invariant features . The main research axis of this thesis is the study of prosody for speech synthesis and emphasis preservation . A first investigation of regional accents of spoken French is carried out to understand the sensitivity of native listeners with respect to accented speech synthesis . Listening tests show that standard adaptation methods for speech synthesis are not sufficient for listeners to perceive accentedness .
Between speech training and speech recognition , we provide speech teachers with an innovative tool for teaching deaf children how to master the articulation of voiced sounds . Two theoretical aspects of this work are important : \bulletPhonemes are seen as elements of an organized structure\bulletThe phonetic recognition is based on rejection principles The following developments derive from those two main ideas:\bulletIn a set of N phonemes , a phoneme is defined by N - 1 relations which the N - 1 possible pairs determine.\bulletIn the recognition phase , when confronting two phonemes , the refutation discards the least probable one .
The speech recognition rate of this method reach ninety-one percents accurately under no noise environment . Speech signal recognition is feasible and effective in noisy environment .
This work introduces a Brazilian Speech Database ( BrSD ) , a novel dataset freely available created to support the development of speech-based recognition tasks . As far as we know , this is the first Portuguese language based database with these characteristics created and made available to the research community .
We find several interesting interactions which suggest that the discourse structure can play an important role in several dialogue related tasks : automatic detection of SRP and analyzing spoken dialogues systems with a large state space from limited amounts of available data . Index Terms : discourse structure , speech recognition analysis , spoken dialogue systems .
This approach contrasts sharply with the typical method of creating such a dictionary by human experts , which can be a time-consuming and expensive endeavor . Our experiments show that automatically derived lexicons allow us to build speech recognizers that consistently perform closely to supervised speech recognizers , which should enable more rapid development of speech recognition capability for low-resource languages .
section : Introduction Over the last years , thanks in part to steady improvements brought by deep learning approaches to speech recognition , voice interfaces have greatly evolved from spotting limited and predetermined keywords to understanding arbitrary formulations of a given intention . They also became much more reliable , with state - of - the - art speech recognition engines reaching human level in English .
Recurrent neural networks ( RNNs ) , such as long short - term memory networks ( LSTMs ) hochreiter1997long , form an important building block for many tasks that require modeling of sequential data . RNNs have been successfully employed for several such tasks including language modeling melis2017state , merity2017regularizing , speech recognition xiong2017microsoft , graves2013speech , and machine translation wu2016google , bahdanau2014neural .
Computational costs and numerical instability led to these techniques to be abandoned for almost a decade . Recently , distributed computing allowed for larger models to be trained [ reference ] , and the growth of data for unsupervised learning algorithms to emerge [ reference ] . Distributed representations usually are trained through neural networks , these networks have made advancements in diverse fields such as computer vision [ reference ] , speech recognition [ reference ] , and natural language processing [ reference ] . section : CONCLUSIONS
Also , compared to concatenation , our method does not significantly increase the number of parameters . Highway LSTMs and depth - gated LSTMs are similar to our proposed models in that they use cell states from the previous layer , and they are successfully applied to the field of automatic speech recognition and language modeling . However in contrast to CAS - LSTM , where the additional forget gate aggregates the previous layer states , and thus contexts from the left and below participate in computation equitably , in Highway LSTMs and depth - gated LSTMs the previous layer states are considered only through peephole connections .
TitouanParcollet , YingZhang , MohamedMorchid , ChihebTrabelsi , GeorgesLinarès , RenatoDeMori andYoshuaBengio Universitéd’Avignon , LIA , France UniversitédeMontréal , MILA , Canada McGillUniversity , Montréal , Canada Orkis , Aixenprovence , France ElementAI , Montréal , Canada titouan.parcollet@alumni.univ - avignon.fr , ying.zhlisa@gmail.com , mohamed.morchid@univ - avignon.fr , chiheb.trabelsi@polymtl.ca , georges.linares@univ - avignon.fr , rdemori@cs.mcgill.ca Index Terms : quaternion convolutional neural networks , automatic speech recognition , deep learning section : Introduction
section : Introduction Recurrent neural networks ( RNNs ) like long short - term memory ( LSTM ; ) networks and gated recurrent unit ( GRU ; ) are popular architectures for sequence modeling tasks like language generation , translation , speech synthesis , and machine comprehension . However , they are harder to optimize compared to feed - forward networks due to challenges like variable length input sequences , repeated application of the same transition operator at each time step , and largely - dense embedding matrix that depends on the vocabulary size .
Further work is needed to tackle this from of instability . We think that extending this framework to other domains such as video ( for frame prediction ) and audio ( pre - trained features for speech synthesis ) should be very interesting . Further investigations into the properties of the learnt latent space would be interesting as well .
In our settings , the input sequences for the th biLSTM layer are the concatenated outputs of all the previous layers , plus the original word embedding sequence . This gives a shortcut connection style setup , related to the widely used idea of residual connections in CNNs for computer vision , highway networks for RNNs in speech processing , and shortcut connections in hierarchical multitasking learning ; but in our case we feed in all the previous layers ’ output sequences as well as the word embedding sequence to every layer . Let represent words in the source sentence .
Since our proposed method can be used independently for noise reduction , it can be combined with the traditional noise reduction methods to further reduce the noise effect . We tested our proposed method as a front-end for robust speech recognition on the AURORA-2J data corpus . Two advanced noise reduction methods , ETSI advanced front-end ( AFE ) method , and particle filtering ( PF ) with minimum mean square error ( MMSE ) estimation method , are used for comparison and combinations .
This paper proposes a speech feature extraction method that utilizes periodicity and nonperiodicity for robust automatic speech recognition . The method was motivated by the auditory comb filtering hypothesis proposed in speech perception research .
An unavoidable problem in speech technology , particularly in the development of robust automatic speech recognition systems , is the extreme variability in the acoustic attributes of segments . Segments are highly sensitive to context and bear little resemblance to their intrinsic characteristics manifested when they are uttered in isolation .
This paper addresses the robust speech recognition problem as an adaptation task . Specifically , we investigate the cumulative application of adaptation methods .
Speech feature extraction has been a key focus in robust speech recognition research . Selecting proper features is the key of effective system performance .
The tracker incorporates predictive dynamics which can be learned from training sequences and automatically tuned to follow typical motions found in speech . The visual data from the tracker is incorporated into an acoustic automatic speech recogniser enabling robust recognition of speech in the presence of acoustic noise . Tests on a 40 word vocabulary using a dynamic time warping based audio-visual recogniser demonstrate that the lip outline is a rich source of information for speech recognition and establish dynamic contour tracking as a viable instrument for near real-time speechreading applications .
This paper studies the use of hybrid context-dependent Deep Neural Network Hidden Markov Model ( DNN-HMM ) architecture for robust recognition of speech affected by realworld nonlinear distortions . We consider two types of distortions ; a ) signals distorted through overgained microphone preamplifier in the analog domain and b ) recordings exhibiting unnatural spectral sparseness , caused by excessive denoising or low-bit-rate compression .
In this paper , we study a class of robust automatic speech recognition problem in which mismatches between training and testing conditions exist but an accurate knowledge of the mismatch mechanism is unknown . The only available information is the test data along with a set of pretrained speech models and the decision parameters .
One approach to this problem is to achieve the desired robustness in speech representations used in the ASR . Motivated from studies on robust human speech recognition , we analyse the unsupervised data-driven temporal modulation filter learning for robust feature extraction . In this paper , we compare various unsupervised models for data driven filter learning like convolutional autoencoder ( CAE ) , generative adversarial network ( GAN ) and convolutional restricted Boltzmann machine ( CRBM ) .
Accurate and effective voice activity detection ( VAD ) is a fundamental step for robust speech or speaker recognition . In this study , we proposed a hierarchical framework approach for VAD and speech enhancement .
EVERA is written in an object-oriented framework and provides a high-level graphic user interface to design , observe and to monitor different simulations . We present some applications of the system to the problem of robust speech perception and recognition in hostile synthetic acoustic environments built from some physical model of real rooms ( 4 walls , 1 floor and 1 ceiling ) which include different real world noises .
Experiments show that the proposed dynamic evaluation methodology gives large test time improvements across character and word level language modelling . Our improvements to language modelling have applications to speech recognition and machine translation over longer contexts , including broadcast speech recognition and paragraph level machine translation . Overall , dynamic evaluation is shown to be an effective method for exploiting pattern re - occurrence in sequences .
In this paper , we describe an end - to - end speech system , called “ Deep Speech ” , where deep learning supersedes these processing stages . Combined with a language model , this approach achieves higher performance than traditional methods on hard speech recognition tasks while also being much simpler . These results are made possible by training a large recurrent neural network ( RNN ) using multiple GPUs and thousands of hours of data .
Finally , we introduce multi - scale input features aimed at exploiting more context at negligible computational cost . We evaluate the improvements first on a Babel task for low resource speech recognition , obtaining an absolute 5.77 % WER improvement over the baseline PLP DNN by training our CNN on the combined data of six different languages . We then evaluate the very deep CNNs on the Hub5’00 benchmark ( using the 262 hours of SWB - 1 training data ) achieving a word error rate of 11.8 % after cross - entropy training , a 1.4 % WER improvement ( 10.6 % relative ) over the best published CNN result so far .
This paper addresses the problem of distant speech recognition in reverberant noisy conditions employing a star-shaped microphone array and vector Taylor series VTS compensation . First , a beamformer yields an enhanced single-channel signal by applying convex CVX optimization over three spatial dimensions given the spatio-temporal position of the target speaker as prior knowledge .
This paper presents a new microphone-array post-filtering algorithm for distant speech recognition ( DSR ) . Conventionally , post-filtering methods assume static noise field models , and using this assumption , employ a Wiener filter mechanism for estimating the noise parameters .
Room reverberation and environment noise pose significant challenges for both speech enhancement and distant speech recognition ( DSR ) . However , the rapid progresses achieved recently in the two fields are somehow ignored by each other .
We believe that “ only good signal processing can lead to top ASR performance ” in challenging acoustic environments . This notion leads to a unified deep neural network ( DNN ) framework for distant speech processing that can achieve both high-quality enhanced speech and high-accuracy ASR simultaneously . Our goal is accomplished by two techniques , namely : ( i ) a reverberation-time-aware DNN based speech dereverberation architecture that can handle a wide range of reverberation times to enhance speech quality of reverberant and noisy speech , followed by ( ii ) DNN-based multicondition training that takes both clean-condition and multicondition speech into consideration , leveraging upon an exploitation of the data acquired and processed with multichannel microphone arrays , to improve ASR performance .
In this paper , we describe the REVERB challenge , which is an evaluation campaign that was designed to evaluate such speech enhancement ( SE ) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions . Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single-channel scenario , a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation , based on both real , single-channel , and multichannel recordings . This challenge attracted 27 papers , which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes .
The robustness of this speaker recognition method for the change of the speaking style in close-talking environment was evaluated in [ 2 ] . We integrated this method to the proposed position dependent CMN for distant speaker recognition . Our experiments showed that the proposed method improved the speaker recognition performance remarkably in a distant environment .
For close-talking speaker recognition , a speaker recognition method by combining speaker-specific GMMs with speaker-adapted syllable-based HMMs has been proposed , which shows robustness for the change of the speaking style in a close-talking environment . In this thesis , we extend this combination method to distant speaker recognition and integrate this method with the proposed position-dependent CMN . Speaker recognition experiments were conducted on NTT database ( 22 male speakers ) and Tohoku University and Panasonic database ( 20 male speakers ) .
This paper presents new superdirective beamforming algorithms based on the maximum negentropy ( MN ) criterion for distant automatic speech recognition . The MN beamformer is configured in the generalized sidelobe canceler structure , and uses the weights derived from a delay-and-sum beamformer as the quiescent weight vector .
Microphone array beamforming has been approved to be an effective approach for suppressing adverse interferences . Recently , acoustic beamformers employing neural networks ( NN ) for time-frequency ( T-F ) mask prediction , termed as Mask-BF , have received tremendous interest and shown great benefits as a front-end for distant automatic speech recognition ( ASR ) . However , our preliminary experiments using Mask-BF for ASR task show that the mask model trained with only simulated training data underperforms when the real-recording data appears in the testing stage , where a data mismatch problem occurs .
Although state of the art speech processing has achieved great performance for most widely used languages , little to no efforts were made for under-resourced languages , such as Romanian . This paper focuses on a set of experiments in building a series of acoustic and grammar models for Romanian language , to be used in distant speech recognition scenarios , for voice driven speech applications in intelligent homes or buildings , using previously acquired speech databases in Romanian language , in real life conditions , by our research group .
The problem of distant recording and recognition of speech for the task of voice interaction with automatic inquiry system in noisy environment is considered . The system perceives and analyses sounds arising from limited subspace due to spatial localization of sound sources .
This paper focuses on the methodology of acquiring four different speech corpora with various end-user scenarios in mind . The commands corpus is meant to be used in home automation development , the cough corpus is meant to help research in detecting distress situations , the spontaneous speech corpus will aid in distant speech recognition applications and the multi-room , multi-person , multi-language corpus can be used for research in speaker detection and identification . All these were recorded in the context of a completely automated and functional smart home .
Second , the proposed recursive architecture in the BridgeNet can iteratively improve denoising and recognition performance . The experimental results of BridgeNet showed significant improvements in tackling the distant speech recognition problem , where it achieved up to 13.24 % relative WER reductions on AMI corpus compared to a baseline neural network without teacher 's hints .
However , as no common reverberation criteria for the recognition of reverberant speech have yet been proposed , it has been difficult to estimate its effectiveness . To address this problem in 2007 , we investigated early and late reflections on distanttalking speech recognition to help define suitable reverberation criteria . We here propose new reverberation criteria RSR-Dn ( Reverberant Speech Recognition criteria with Dn ) based on our previous investigations and ISO3382 acoustic parameters .
The author takes the view that improvements in performance will continue to be based on both technological and linguistic knowledge . To illustrate this , the paper reviews ongoing work aimed at overcoming the deficiencies of the current paradigm and elaborates research on speaker adaptation and accented speech recognition . This work offers considerable potential to improve future systems ' performance .
Various techniques , such as acoustic model adaptation and pronunciation adaptation , have been reported to improve the recognition of non-native or accented speech . In this paper , we propose a new approach that combines accent detection , accent discriminative acoustic features , acoustic adaptation and model selection for accented Chinese speech recognition . Experimental results show that this approach can improve the recognition of accented speech .
This parameter is used to adjust the HMM state densities for each input speech frame , leading to both high robustness and good resolution for dynamic tracking to cover a diversity of temporal variations in speech . Tested on an accented speech recognition application , the proposed framework yields an improved syllable error rate reduction over the conventional DGMS and augmented HMM systems when evaluated on three typical Chinese accents , Chuan , Yue and Wu , while maintaining its performance for standard Putonghua .
In this paper , we propose a domain adversarial training ( DAT ) algorithm to alleviate the accented speech recognition problem . In order to reduce the mismatch between labeled source domain data ( “ standard ” accent ) and unlabeled target domain data ( with heavy accents ) , we augment the learning objective for a Kaldi TDNN network with a domain adversarial training ( DAT ) objective to encourage the model to learn accent-invariant features .
The main research axis of this thesis is the study of prosody for speech synthesis and emphasis preservation . A first investigation of regional accents of spoken French is carried out to understand the sensitivity of native listeners with respect to accented speech synthesis . Listening tests show that standard adaptation methods for speech synthesis are not sufficient for listeners to perceive accentedness .
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ICTAI INTERSPEECH ArXiv ArXiv KDD '14 ACML INTERSPEECH ICLR ICLR RepEval@EMNLP INTERSPEECH INTERSPEECH EUROSPEECH INTERSPEECH ICML ArXiv 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IberSPEECH INTERSPEECH ISCSLP INTERSPEECH EUSIPCO APSIPA SpeD SpeD INTERSPEECH INTERSPEECH ICASSP
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