Any preserved information that is irrelevant to the ASR task could complicate further processing . Attributes in conventional ASR are derived by some transformation of the short‐term spectrum of speech that represents magnitude frequency components of a short segment of the signal . Origins of this representation can be traced to speech vocoding . The framework offers a novel mechanism with multi-level feedback loops to modify the feature extraction algorithm and a multi-granularity feature fusion criteria to identify the speeches . This ASR architecture would greatly improve online speech recognition performance . While state-of-the-art , deep neural net based acoustic models have significantly improved the recognition performance of native speaker ’s speech , good recognition is still challenging when the input speech consists of non-native spontaneous utterances . In this paper , we investigate how to train a DNN based ASR with a fairly large non-native English corpus and make it self-adaptive to a test speaker and a new task , namely a simulated conversation , which is different from them monologic speech in the training data . Automated assessment of language proficiency is evaluated according to both task completion ( TC ) and pragmatic competence ( PC ) rubrics . Speech source separation separates target speech from interfering speech but its performance is affected by adverse environmental conditions of acoustical reverberation and background noise . This dissertation works on the enhancement of a speech source separation technique , namely adaptive decorrelation filtering ( ADF ) , for robust ASR applications . To overcome these difficulties and develop practical ADF speech separation algorithms for robust ASR , improvements are introduced in several aspects . The experimental results show that this scheme can further reduce the ASR and improve the QoS under the premise of satisfying users ' location privacy demands . Keywords-continuous LBS queries ; user aggregation ; ASR segmentation ; QoS Despite its success , the practical usage is limited to offline ASR tasks , since encoder-decoder architectures typically require an entire speech utterance as input . In this work , we propose a transformer based end-to-end ASR system for streaming ASR , where an output must be generated shortly after each spoken word . To achieve this , we apply time-restricted self-attention for the encoder and triggered attention for the encoder-decoder attention mechanism . In addition , the empirical distribution of WER for most ASR systems usually tends to put a significant mass near zero , making it difficult to simulate with a single continuous distribution . In order to address the two issues of ASR quality estimation ( QE ) , we propose a novel neural zero-inflated model to predict the WER of the ASR result without transcripts . We design a neural zero-inflated beta regression on top of a bidirectional transformer language model conditional on speech features ( speech-BERT ) . This requires that we are able to generate adaptation transforms from the output of an automatic speech recognition ( ASR ) system . An approach that utilises unified ASR and TTS models would seem to offer an ideal mechanism for the application of unsupervised adaptation to TTS since transforms could be shared between ASR and TTS . Such unified models should use a common set of parameters . A method that can lessen this influence , using both long-term and short-term glottal information , is proposed . Spectral features behave more discriminative in text-independent automatic speaker recognition ( ASR ) through this recuperation . Our method was applied to YOHO corpus and our SRMC corpus . Oracle experiments are also conducted and show the ability of our metric to find better hypotheses ( to be translated ) in the ASR N-best . Finally , a preliminary experiment where ASR tuning is based on our new metric shows encouraging results . For reproductible experiments , the code allowing to call our modified WER and the corpora used are made available to the research community . The application records the input data and recognizes the isolated words in the input file over a wide range of signal to noise ratio ( SNR ) . The experimental results show a remarkable increase of about 10 % in the recognition rate in the AVSR compared to the audio only ASR and 20 % compared to the video only ASR for an SNR of 5 dB. CONCLUSION Without modification , two well-known ASR systems do not perform well in interpreting spoken clinical questions . With a simple domain adaptation , one of the ASR systems improved significantly on the clinical question task , indicating the importance of developing domain/genre-specific ASR systems . ASR Insurance , one of the top 3 insurance companies in the Netherlands is transitioning their IT maintenance and operations from a more traditional approach to Kanban . They started small with 1 team and slowly increased to 7 teams to gain experience . This paper provides a detailed description of the University of Washington automatic speech recognition ( ASR ) system for the 2001 DARPA SPeech In Noisy Environments ( SPINE ) task . Our system makes heavy use of the graphical modeling toolkit ( GMTK ) , a general purpose graphical modeling-based ASR system that allows arbitrary parameter tying , flexible deterministic and stochastic dependencies between variables , and a generalized maximum likelihood parameter estimation algorithm . In our SPINE system , GMTK was used for acoustic model training whereas feature extraction , speaker adaptation , and first-pass decoding were performed by HTK . Driven decoding algorithm ( DDA ) is initially an integrated approach for the combination of 2 speech recognition ( ASR ) systems . It consists in guiding the search algorithm of a primary ASR system by the one-best hypothesis of an auxiliary system . In this paper , we generalize DDA to confusion-network driven decoding and we propose new combination schemes for multiple system combination . An electric wheel vertical loading system ( 7 ) is installed on an electric wheel loading system support ( 9 ) . The stand is connected to a host computer during testing , can achieve testing of mechanical properties , efficiency properties and adjustment properties of an electric wheel hub motor , and provides testing means for ASR system simulation and control strategy studies . The stand can test basic properties of an electric wheel motor and provides bases for design of a vehicle controller . Different from the typical DNN estimation , our DNN-based phoneme estimation approach is to use output patterns of phonemes by multiple ASR systems as input features to DNN . The characteristic of our method enables more improvement of the ASR performance of a start-of-the-art DNN-Hidden Markov Model-based ASR system because our method uses a perfectly different type of the input features to a DNN . The DNN-based correct phoneme estimator was applied to the STD tasks . Soon after , interest moved over to hybrid systems that combine HMMs and ANNs within a single unifying hybrid architecture . In this study a hybrid DTW/ANN ASR system will be introduced , explained , implemented and analyzed , which has been given the name Relative Distance Vector Neural Network ( RDVNN ) Model . Adequate experiments had been performed to reveal the main characteristics of the present novel hybrid ASR system . We compare results of previous experiments which showed that prosody improves the detection of ASR errors to experiments with a new system and new domain , the W99 conference registration system . Our new results again show that prosodic features can improve prediction of ASR misrecognitions over the use of other standard techniques for ASR rejection . The purpose was retrieving information from the Internet using speech as primary input . The Android application uses an ASR front-end to recognize uttered words and transform them into text patterns . Then it exploits on board GPS data and RESTful Twitter API to find twits containing the recognized text and coming from users located in a certain area around the device . A commonly employed technique in computer vision during network evaluation is to randomly jitter inputs by translations or reflections , feed each jittered version through the network , and vote or average the results . Such jittering is not common in ASR , however we found it beneficial to translate the raw audio files by 5ms ( half the filter bank step size ) to the left and right , then forward propagate the recomputed features and average the output probabilities . At test time we also use an ensemble of several RNNs , averaging their outputs in the same way . Recent results in dialogue state tracking show that it is beneficial to jointly learn speech understanding and dialogue tracking . These approaches directly take as input the N - best list produced by the ASR system . By avoiding the accumulation of errors from the initial SLU component , these joint approaches typically achieved stronger performance on tasks such as DSTC2 . First case in point : front - end processing has been simplified considerably with the use of CNNs which treat the log - mel spectral representation as an image and do n’t require extra processing steps such as PLP cepstra , LDA , FMLLR , fMPE transforms , etc . Second case in point : end - to - end ASR systems such as bypass the need of having phonetic context decision trees and HMMs altogether and directly map the sequence of acoustic features to a sequence of characters or context independent phones . Third case in point : training algorithms such as connectionist temporal classification The IARPA Babel program is aimed at developing robust keyword search technology for low resource languages . Though the word error rates reported here are too high to be useful for simple speech to text applications , useful keyword search ( KWS ) systems can still be built based on these ASR models . As training data we use a combination of 6 languages , with 3 hours of training data per language . subsection : Recognition performance on CHiME In this sub - section we extend the results to the CHiME corpus , that is an important benchmark in the ASR field , thanks to the success of CHiME challenges . In Tab . We select different sets of STRFs which are specific for phonemes in different broad phonetic classes ( BPC ) of sounds . These STRFs are then used as spectro-temporal filters on spectrograms of speech to extract features for phoneme recognition . For the phoneme recognition task on the TIMIT database , the proposed features show a relative improvement of about 5 % over conventional feature extraction techniques . Based on the Sotscheck text corpus , we developped a new corpus that was specifically optimised for training phoneme-based recognition systems . Particular attention was payed on good coverage of phone transitions . Hence , we introduce a reformulation of the standard SVM that incorporates the degree of belief . Experimental performance on TIMIT database shows the effectiveness of the proposed method B-SVM on a phoneme recognition problem . But it produces a relatively lower recognition rate . To solve that problem , a vocabulary recognition-model optimization method is proposed based on a similar phoneme – recognition process and efficient feature extraction . In vocabulary recognition , a similar phoneme – recognition process is applied to HMM to recognize models adjacent to the model group . Firstly , a recurrent neural networks architecture is developed to accommodate the output of the VAD stage . Specifically , an Echo State Network ( ESN ) is used for phoneme level recognition . The drawbacks and advantages of this method are explained further within the thesis . The implementation of vowel , vowel-like , and non-vowel segmentation in the digit decision tree shows that an efficient digit recognition algorithm can be achieved . Though the first phoneme recognition scheme is found to be satisfactory , the second scheme produces better results . Similar results are obtained for digits spoken in Arabic . The system is composed of the phoneme recognition part and the phrase recognition part . In the phoneme recognition part , the features in the articulatory domain are extracted and applied to compensate coarticulation . In the phrase recognition part , a word sequence corresponding to the phoneme sequence is determined by using two-level DP matching with automaton control , in which words are processed symbolically to attain the acceptable processing speed . We then describe our initial work towards porting Sonic , the CSLR speech recognition system , to the Turkish language . Results are shown for phonetic alignment and phoneme recognition accuracy using the newly constructed corpus and speech tools . It is shown that 91.2 % of the automatically labeled phoneme boundaries are placed within 20 msec of hand-labeled locations for the Turkish audio corpus . Computing was realized by using MatLAB 7 . The results of phoneme recognition analysis has allowed well quality ( 95 % ) . Still , the language model was added only at the last stage in that work , thus leaving open a question of how much an acoustic model can benefit from being aware of a language model during training . In this paper , we evaluate attention - based models on a phoneme recognition task using the widely - used TIMIT dataset . At each time step in generating an output sequence ( phonemes ) , an attention mechanism selects or weighs the signals produced by a trained feature extraction mechanism at potentially all of the time steps in the input sequence ( speech frames ) . Merge a quaternion convolutional neural network ( QCNN , Section [ reference ] ) with the CTC in a unified and easily reusable framework . Compare and evaluate the effectiveness of the proposed QCNN to an equivalent real - valued model on the TIMIT phonemes recognition task ( Section [ reference ] ) . There are advantages which could derive from bundling groups of numbers into a quaternion . In terms of PER , an average relative gain of % ( w.r.t CNNs result ) is obtained on the testing set . It is also worth recalling that the best PER of % is obtained with just a QCNN without HMMs , RNNs , attention mechanisms , batch normalization , phoneme language model , acoustic data normalization or adaptation . Further improvements can be obtained with exactly the same QCNN by just introducing a new acoustic feature in the real part of the quaternions . section : Related work Early attempts to perform phoneme and phonetic feature recognition with multilayer perceptrons ( MLP ) were proposed in . A PER of % is reported in using RNNs . More recently , in a Mean - Covariance Restricted Boltzmann Machine ( RBM ) is used for recognizing phonemes in the TIMIT corpus using RBM for feature extraction . Along this line of research , in an approach called the Connectionist Temporal Classification ( CTC ) has been developed and can be used without an explicit input - output alignment . Nevertheless , speech science has shown that other multi - view context - dependent acoustic relations characterize signals of phonemes in context . Future work will attempt to characterize those multi - view features that mostly contribute to reduce ambiguities in representing phoneme events . Furthermore , quaternions - valued RNNs will also be investigated to see if they can contribute to the improvement of recently achieved top of the line results with real number RNNs . This paper investigates deep recurrent neural networks , which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs . When trained end - to - end with suitable regularisation , we find that deep Long Short - term Memory RNNs achieve a test set error of 17.7 % on the TIMIT phoneme recognition benchmark , which to our knowledge is the best recorded score . AlexGraves , Abdel - rahmanMohamedandGeoffreyHinton DepartmentofComputerScience , UniversityofToronto recurrent neural networks , deep neural networks , speech recognition In particular , we concentrate on the potential benefits of pseudo-articulatory features in adverse acoustic environments and on their combination with standard acoustic features . Systems based on articulatory features only and combined acoustic-articulatory systems are tested on two different recognition tasks : telephone-speech continuous numbers recognition and conversational speech recognition . We show that articulatory feature ( AF ) systems are capable of achieving a superior performance at high noise levels and that the combination of acoustic and AFs consistently leads to a significant reduction of word error rate across all acoustic conditions . In such occasions , humans tend to produce disfluencies as a listener-oriented strategy to signal the ongoing production process and to buy time for finalizing the turn . Introducing disfluency capabilities into a speech synthesis module of a dialogue system may therefore be a straightforward strategy towards conversational speech systems . Disfluencies are a very complex matter , they can take various chaining and nested forms in human communication . The positive-negative attitude can be controlled by average F0 height while those of confident-doubtful or allowable-unacceptable are manifested through dynamic F0 patterns . These findings provide new possibilities of systematic F0 control for conversational speech synthesis with speaking attitudes using a corpus-based approach . Results show that turn taking rate and overlapping speech segments under certain conditions correlate with deviations from normal driving patterns . The conversational speech analysis can thus be utilized as a component in driver assistance systems such that the impact of in-vehicle speech activity on driving performance is controlled or minimized . The casual speaking style and lower “ fact density ” typical of spontaneous conversations make named entity extraction less effective than in other domains and leads us to consider extraction of descriptive noun phrases as a more powerful generalization of named entities . We describe a system for finding semantically important noun phrases that combines our conversational speech recognizer with a probabilistic model of syntax and semantics . We present preliminary experiments on a subset of the Switchboard corpus . We propose two adaptation models for recurrent neural network language models ( RNNLMs ) to capture topic effects and longdistance triggers for conversational automatic speech recognition ( ASR ) . We use a fast marginal adaptation ( FMA ) framework to adapt a RNNLM . A speech therapy game has the potential to make practice fun , may help facilitate increased time and quality of at-home speech therapy , and lead to improved speech . We explore using conversational real-time speech recognition , game methodologies theorized to improve immersion and flow , and user centered approaches to design an immersive interactive speech therapy solution . Our preliminary user evaluation showed that compared to traditional methods , children were more motivated to practice speech using our system . SND and diarization can then be used to analyze social interactions . In this research privacy preserving audio features has been investigated instead for recording and storage that can respect privacy by minimizing the amount of linguistic information , whereas achieving modern performance in conversational speech processing tasks . Certainly , the main contribution of the proposed system is the achievement of state-of-the-art performances in speech/nonspeech detection and speaker diarization tasks using such features , which we refer to , as privacy-sensitive . PURPOSE This research note discusses a common misconception in speech science and speech-language pathology textbooks that rib torque ( i.e. , rotational stress ) assists resting tidal expiration and conversational speech production . METHOD The nature of this misconception is considered . document : Achieving Human Parity in Conversational Speech Recognition Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s . In this paper , we measure the human error rate on the widely used NIST 2000 test set , and find that our latest automated system has reached human parity . In the meantime , ensemble learning has become commonly used in several neural models , to improve robustness by reducing bias and variance . In this paper , we use ensembles of models extensively , as well as improvements to individual component models , to to advance the state - of - the - art in conversational telephone speech recognition ( CTS ) , which has been a benchmark speech recognition task since the 1990s . The main features of this system are : An ensemble of two fundamental acoustic model architectures , convolutional neural nets ( CNNs ) and long - short - term memory nets ( LSTMs ) , with multiple variants of each An attention mechanism in the LACE CNN which differentially weights distant context Lattice - free MMI training The use of i - vector based adaptation in all models Language model ( LM ) rescoring with multiple , recurrent neural net LMs running in both forward and reverse direction Confusion network system combination coupled with search for best system subset , as necessitated by the large number of candidate systems . In this paper , we experiment with the recently introduced subword regularization technique [ 1 ] in the context of end-to-end automatic speech recognition ( ASR ) . We present results from both attention-based and CTC-based ASR systems on two common benchmark datasets , the 80 hour Wall Street Journal corpus and 1,000 hour Librispeech corpus . Experiments on the IEMOCAP dataset demonstrate that the auxiliary learning tasks improve emotion classification accuracy compared to a baseline supervised classifier . Further , we demonstrate that the proposed learning approach can be used for the end-to-end speech emotion recognition , as its applicable for models that operate on frame-level inputs . To address this problem , we propose to augment a natural speech dataset with synthetic speech . We train very large end-to-end neural speech recognition models using the LibriSpeech dataset augmented with synthetic speech . These new models achieve state of the art Word Error Rate ( WER ) for character-level based models without an external language model . Recently , many emotion researchers trend the direction to automatic emotion recognition from the raw signal , the motivation behind this is that neural network can learn representation and find the final result automatically . This work focuses on categorization and reviews on the current progress on end-to-end speech emotion recognition problems . In this survey , we discuss the requirement of the network model , process procedures and current achiements . The acoustic - level predictions are later embedded in an HMM - based framework , that also integrates phone - transitions , lexicon , and language model information to retrieve the final sequence of words . An emerging alternative is end - to - end speech recognition , that aims to drastically simplify the current ASR pipeline by using fully discriminative systems that learn everything from data without ( ideally ) any additional human effort . Popular end - to - end techniques are attention models and Connectionist Temporal Classification ( CTC ) . document : Quaternion Convolutional Neural Networks for End - to - End Automatic Speech Recognition Recently , the connectionist temporal classification ( CTC ) model coupled with recurrent ( RNN ) or convolutional neural networks ( CNN ) , made it easier to train speech recognition systems in an end - to - end fashion . Top speech recognition systems rely on sophisticated pipelines composed of multiple algorithms and hand - engineered processing stages . 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 . For these reasons speech recognition is an interesting testbed for developing new attention - based architectures capable of processing long and noisy inputs . Application of attention - based models to speech recognition is also an important step toward building fully end - to - end trainable speech recognition systems , which is an active area of research . The dominant approach is still based on hybrid systems consisting of a deep neural acoustic model , a triphone HMM model and an n - gram language model . We show that confidence measures estimated from local posterior probabilities can serve as objective functions for training ANNs in hybrid HMM based speech recognition systems . This leads to a segment-level training paradigm that overcomes the limitation of frame-level updates ignoring the sequence structure in speech . However , only a few methods rely on tuning the connection weights in trained DNNs directly to optimize system performance since it is very prone to over-fitting especially when some class labels are missing in the adaptation data . In this paper , we propose a new speaker adaptation method for the hybrid NN/HMM speech recognition model based on singular value decomposition ( SVD ) . We apply SVD on the weight matrices in trained DNNs and then tune rectangular diagonal matrices with the adaptation data . This paper presents a method to improve the recognition rate of hybrid connectionist/HMM speech recognition systems . At the same time this approach allows the easy introduction of context dependent models in the hybrid framework . This paper presents the results of a large number of experiments performed on the Hiwire cockpit database with a hybrid HMM-ANN speech recognition model . The Hiwire database is a noisy and non-native English speech corpus for cockpit communication . A recent development in the hybrid HMM/ANN speech recognition paradigm is the use of several subword classifiers , each of which provides different information about the speech signal . Although the combining methods have obtained promising results , the combination strategies so far proposed have been relatively simple . In the present study , we address this issue by investigating variants of large receptive field CNNs ( LRF-CNNs ) which include deeply recursive networks , dilated convolutional neural networks , and stacked hourglass networks . To compare the efficacy of the aforementioned architectures with the standard CNN for Wall Street Journal ( WSJ ) corpus , we use a hybrid DNN-HMM based speech recognition system . We extend the study to evaluate the system performances for distant speech simulated using realistic room impulse responses ( RIRs ) . A previously proposed and very promising boosting algorithm is AdaBoost . In this paper we investigate if AdaBoost can be used to improve a hybrid HMM/neural network continuous speech recognizer . Boosting significantly improves the word error rate from 6.3 % to 5.3 % on a test set of the OGI Numbers 95 corpus , a medium size continuous numbers recognition task . The aim of this work is to develop a practical framework , which extends the classical Hidden Markov Models ( HMM ) for continuous speech recognition based on the Maximum Entropy ( MaxEnt ) principle . The MaxEnt models can estimate the posterior probabilities directly as with Hybrid NN/HMM connectionist speech recognition systems . In particular , a new acoustic modelling based on discriminative MaxEnt models is formulated and is being developed to replace the generative Gaussian Mixture Models ( GMM ) commonly used to model acoustic variability . As illustrated in fig : brnn , a BRNN computes the forward hidden sequence , the backward hidden sequence and the output sequence by iterating the backward layer from to , the forward layer from to and then updating the output layer : Combing BRNNs with LSTM gives bidirectional LSTM , which can access long - range context in both input directions . A crucial element of the recent success of hybrid HMM - neural network systems is the use of deep architectures , which are able to build up progressively higher level representations of acoustic data . Deep RNNs can be created by stacking multiple RNN hidden layers on top of each other , with the output sequence of one layer forming the input sequence for the next . subsection : Very deep convolutional networks Very deep CNNs with small kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN - HMM speech recognition systems . Results were provided after cross - entropy training on the 300 hours switchboard - 1 dataset in , and results from sequence training on both switchboard - 1 and the 2000 hours switchboard + Fisher dataset are in .
Any preserved information that is irrelevant to the ASR task could complicate further processing . Attributes in conventional ASR are derived by some transformation of the short‐term spectrum of speech that represents magnitude frequency components of a short segment of the signal . Origins of this representation can be traced to speech vocoding .
The framework offers a novel mechanism with multi-level feedback loops to modify the feature extraction algorithm and a multi-granularity feature fusion criteria to identify the speeches . This ASR architecture would greatly improve online speech recognition performance .
While state-of-the-art , deep neural net based acoustic models have significantly improved the recognition performance of native speaker ’s speech , good recognition is still challenging when the input speech consists of non-native spontaneous utterances . In this paper , we investigate how to train a DNN based ASR with a fairly large non-native English corpus and make it self-adaptive to a test speaker and a new task , namely a simulated conversation , which is different from them monologic speech in the training data . Automated assessment of language proficiency is evaluated according to both task completion ( TC ) and pragmatic competence ( PC ) rubrics .
Speech source separation separates target speech from interfering speech but its performance is affected by adverse environmental conditions of acoustical reverberation and background noise . This dissertation works on the enhancement of a speech source separation technique , namely adaptive decorrelation filtering ( ADF ) , for robust ASR applications . To overcome these difficulties and develop practical ADF speech separation algorithms for robust ASR , improvements are introduced in several aspects .
The experimental results show that this scheme can further reduce the ASR and improve the QoS under the premise of satisfying users ' location privacy demands . Keywords-continuous LBS queries ; user aggregation ; ASR segmentation ; QoS
Despite its success , the practical usage is limited to offline ASR tasks , since encoder-decoder architectures typically require an entire speech utterance as input . In this work , we propose a transformer based end-to-end ASR system for streaming ASR , where an output must be generated shortly after each spoken word . To achieve this , we apply time-restricted self-attention for the encoder and triggered attention for the encoder-decoder attention mechanism .
In addition , the empirical distribution of WER for most ASR systems usually tends to put a significant mass near zero , making it difficult to simulate with a single continuous distribution . In order to address the two issues of ASR quality estimation ( QE ) , we propose a novel neural zero-inflated model to predict the WER of the ASR result without transcripts . We design a neural zero-inflated beta regression on top of a bidirectional transformer language model conditional on speech features ( speech-BERT ) .
This requires that we are able to generate adaptation transforms from the output of an automatic speech recognition ( ASR ) system . An approach that utilises unified ASR and TTS models would seem to offer an ideal mechanism for the application of unsupervised adaptation to TTS since transforms could be shared between ASR and TTS . Such unified models should use a common set of parameters .
A method that can lessen this influence , using both long-term and short-term glottal information , is proposed . Spectral features behave more discriminative in text-independent automatic speaker recognition ( ASR ) through this recuperation . Our method was applied to YOHO corpus and our SRMC corpus .
Oracle experiments are also conducted and show the ability of our metric to find better hypotheses ( to be translated ) in the ASR N-best . Finally , a preliminary experiment where ASR tuning is based on our new metric shows encouraging results . For reproductible experiments , the code allowing to call our modified WER and the corpora used are made available to the research community .
The application records the input data and recognizes the isolated words in the input file over a wide range of signal to noise ratio ( SNR ) . The experimental results show a remarkable increase of about 10 % in the recognition rate in the AVSR compared to the audio only ASR and 20 % compared to the video only ASR for an SNR of 5 dB.
CONCLUSION Without modification , two well-known ASR systems do not perform well in interpreting spoken clinical questions . With a simple domain adaptation , one of the ASR systems improved significantly on the clinical question task , indicating the importance of developing domain/genre-specific ASR systems .
ASR Insurance , one of the top 3 insurance companies in the Netherlands is transitioning their IT maintenance and operations from a more traditional approach to Kanban . They started small with 1 team and slowly increased to 7 teams to gain experience .
This paper provides a detailed description of the University of Washington automatic speech recognition ( ASR ) system for the 2001 DARPA SPeech In Noisy Environments ( SPINE ) task . Our system makes heavy use of the graphical modeling toolkit ( GMTK ) , a general purpose graphical modeling-based ASR system that allows arbitrary parameter tying , flexible deterministic and stochastic dependencies between variables , and a generalized maximum likelihood parameter estimation algorithm . In our SPINE system , GMTK was used for acoustic model training whereas feature extraction , speaker adaptation , and first-pass decoding were performed by HTK .
Driven decoding algorithm ( DDA ) is initially an integrated approach for the combination of 2 speech recognition ( ASR ) systems . It consists in guiding the search algorithm of a primary ASR system by the one-best hypothesis of an auxiliary system . In this paper , we generalize DDA to confusion-network driven decoding and we propose new combination schemes for multiple system combination .
An electric wheel vertical loading system ( 7 ) is installed on an electric wheel loading system support ( 9 ) . The stand is connected to a host computer during testing , can achieve testing of mechanical properties , efficiency properties and adjustment properties of an electric wheel hub motor , and provides testing means for ASR system simulation and control strategy studies . The stand can test basic properties of an electric wheel motor and provides bases for design of a vehicle controller .
Different from the typical DNN estimation , our DNN-based phoneme estimation approach is to use output patterns of phonemes by multiple ASR systems as input features to DNN . The characteristic of our method enables more improvement of the ASR performance of a start-of-the-art DNN-Hidden Markov Model-based ASR system because our method uses a perfectly different type of the input features to a DNN . The DNN-based correct phoneme estimator was applied to the STD tasks .
Soon after , interest moved over to hybrid systems that combine HMMs and ANNs within a single unifying hybrid architecture . In this study a hybrid DTW/ANN ASR system will be introduced , explained , implemented and analyzed , which has been given the name Relative Distance Vector Neural Network ( RDVNN ) Model . Adequate experiments had been performed to reveal the main characteristics of the present novel hybrid ASR system .
We compare results of previous experiments which showed that prosody improves the detection of ASR errors to experiments with a new system and new domain , the W99 conference registration system . Our new results again show that prosodic features can improve prediction of ASR misrecognitions over the use of other standard techniques for ASR rejection .
The purpose was retrieving information from the Internet using speech as primary input . The Android application uses an ASR front-end to recognize uttered words and transform them into text patterns . Then it exploits on board GPS data and RESTful Twitter API to find twits containing the recognized text and coming from users located in a certain area around the device .
A commonly employed technique in computer vision during network evaluation is to randomly jitter inputs by translations or reflections , feed each jittered version through the network , and vote or average the results . Such jittering is not common in ASR , however we found it beneficial to translate the raw audio files by 5ms ( half the filter bank step size ) to the left and right , then forward propagate the recomputed features and average the output probabilities . At test time we also use an ensemble of several RNNs , averaging their outputs in the same way .
Recent results in dialogue state tracking show that it is beneficial to jointly learn speech understanding and dialogue tracking . These approaches directly take as input the N - best list produced by the ASR system . By avoiding the accumulation of errors from the initial SLU component , these joint approaches typically achieved stronger performance on tasks such as DSTC2 .
First case in point : front - end processing has been simplified considerably with the use of CNNs which treat the log - mel spectral representation as an image and do n’t require extra processing steps such as PLP cepstra , LDA , FMLLR , fMPE transforms , etc . Second case in point : end - to - end ASR systems such as bypass the need of having phonetic context decision trees and HMMs altogether and directly map the sequence of acoustic features to a sequence of characters or context independent phones . Third case in point : training algorithms such as connectionist temporal classification
The IARPA Babel program is aimed at developing robust keyword search technology for low resource languages . Though the word error rates reported here are too high to be useful for simple speech to text applications , useful keyword search ( KWS ) systems can still be built based on these ASR models . As training data we use a combination of 6 languages , with 3 hours of training data per language .
subsection : Recognition performance on CHiME In this sub - section we extend the results to the CHiME corpus , that is an important benchmark in the ASR field , thanks to the success of CHiME challenges . In Tab .
We select different sets of STRFs which are specific for phonemes in different broad phonetic classes ( BPC ) of sounds . These STRFs are then used as spectro-temporal filters on spectrograms of speech to extract features for phoneme recognition . For the phoneme recognition task on the TIMIT database , the proposed features show a relative improvement of about 5 % over conventional feature extraction techniques .
Based on the Sotscheck text corpus , we developped a new corpus that was specifically optimised for training phoneme-based recognition systems . Particular attention was payed on good coverage of phone transitions .
Hence , we introduce a reformulation of the standard SVM that incorporates the degree of belief . Experimental performance on TIMIT database shows the effectiveness of the proposed method B-SVM on a phoneme recognition problem .
But it produces a relatively lower recognition rate . To solve that problem , a vocabulary recognition-model optimization method is proposed based on a similar phoneme – recognition process and efficient feature extraction . In vocabulary recognition , a similar phoneme – recognition process is applied to HMM to recognize models adjacent to the model group .
Firstly , a recurrent neural networks architecture is developed to accommodate the output of the VAD stage . Specifically , an Echo State Network ( ESN ) is used for phoneme level recognition . The drawbacks and advantages of this method are explained further within the thesis .
The implementation of vowel , vowel-like , and non-vowel segmentation in the digit decision tree shows that an efficient digit recognition algorithm can be achieved . Though the first phoneme recognition scheme is found to be satisfactory , the second scheme produces better results . Similar results are obtained for digits spoken in Arabic .
The system is composed of the phoneme recognition part and the phrase recognition part . In the phoneme recognition part , the features in the articulatory domain are extracted and applied to compensate coarticulation . In the phrase recognition part , a word sequence corresponding to the phoneme sequence is determined by using two-level DP matching with automaton control , in which words are processed symbolically to attain the acceptable processing speed .
We then describe our initial work towards porting Sonic , the CSLR speech recognition system , to the Turkish language . Results are shown for phonetic alignment and phoneme recognition accuracy using the newly constructed corpus and speech tools . It is shown that 91.2 % of the automatically labeled phoneme boundaries are placed within 20 msec of hand-labeled locations for the Turkish audio corpus .
Computing was realized by using MatLAB 7 . The results of phoneme recognition analysis has allowed well quality ( 95 % ) .
Still , the language model was added only at the last stage in that work , thus leaving open a question of how much an acoustic model can benefit from being aware of a language model during training . In this paper , we evaluate attention - based models on a phoneme recognition task using the widely - used TIMIT dataset . At each time step in generating an output sequence ( phonemes ) , an attention mechanism selects or weighs the signals produced by a trained feature extraction mechanism at potentially all of the time steps in the input sequence ( speech frames ) .
Merge a quaternion convolutional neural network ( QCNN , Section [ reference ] ) with the CTC in a unified and easily reusable framework . Compare and evaluate the effectiveness of the proposed QCNN to an equivalent real - valued model on the TIMIT phonemes recognition task ( Section [ reference ] ) . There are advantages which could derive from bundling groups of numbers into a quaternion .
In terms of PER , an average relative gain of % ( w.r.t CNNs result ) is obtained on the testing set . It is also worth recalling that the best PER of % is obtained with just a QCNN without HMMs , RNNs , attention mechanisms , batch normalization , phoneme language model , acoustic data normalization or adaptation . Further improvements can be obtained with exactly the same QCNN by just introducing a new acoustic feature in the real part of the quaternions . section : Related work Early attempts to perform phoneme and phonetic feature recognition with multilayer perceptrons ( MLP ) were proposed in . A PER of % is reported in using RNNs . More recently , in a Mean - Covariance Restricted Boltzmann Machine ( RBM ) is used for recognizing phonemes in the TIMIT corpus using RBM for feature extraction . Along this line of research , in an approach called the Connectionist Temporal Classification ( CTC ) has been developed and can be used without an explicit input - output alignment .
Nevertheless , speech science has shown that other multi - view context - dependent acoustic relations characterize signals of phonemes in context . Future work will attempt to characterize those multi - view features that mostly contribute to reduce ambiguities in representing phoneme events . Furthermore , quaternions - valued RNNs will also be investigated to see if they can contribute to the improvement of recently achieved top of the line results with real number RNNs .
This paper investigates deep recurrent neural networks , which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs . When trained end - to - end with suitable regularisation , we find that deep Long Short - term Memory RNNs achieve a test set error of 17.7 % on the TIMIT phoneme recognition benchmark , which to our knowledge is the best recorded score . AlexGraves , Abdel - rahmanMohamedandGeoffreyHinton DepartmentofComputerScience , UniversityofToronto recurrent neural networks , deep neural networks , speech recognition
In particular , we concentrate on the potential benefits of pseudo-articulatory features in adverse acoustic environments and on their combination with standard acoustic features . Systems based on articulatory features only and combined acoustic-articulatory systems are tested on two different recognition tasks : telephone-speech continuous numbers recognition and conversational speech recognition . We show that articulatory feature ( AF ) systems are capable of achieving a superior performance at high noise levels and that the combination of acoustic and AFs consistently leads to a significant reduction of word error rate across all acoustic conditions .
In such occasions , humans tend to produce disfluencies as a listener-oriented strategy to signal the ongoing production process and to buy time for finalizing the turn . Introducing disfluency capabilities into a speech synthesis module of a dialogue system may therefore be a straightforward strategy towards conversational speech systems . Disfluencies are a very complex matter , they can take various chaining and nested forms in human communication .
The positive-negative attitude can be controlled by average F0 height while those of confident-doubtful or allowable-unacceptable are manifested through dynamic F0 patterns . These findings provide new possibilities of systematic F0 control for conversational speech synthesis with speaking attitudes using a corpus-based approach .
Results show that turn taking rate and overlapping speech segments under certain conditions correlate with deviations from normal driving patterns . The conversational speech analysis can thus be utilized as a component in driver assistance systems such that the impact of in-vehicle speech activity on driving performance is controlled or minimized .
The casual speaking style and lower “ fact density ” typical of spontaneous conversations make named entity extraction less effective than in other domains and leads us to consider extraction of descriptive noun phrases as a more powerful generalization of named entities . We describe a system for finding semantically important noun phrases that combines our conversational speech recognizer with a probabilistic model of syntax and semantics . We present preliminary experiments on a subset of the Switchboard corpus .
We propose two adaptation models for recurrent neural network language models ( RNNLMs ) to capture topic effects and longdistance triggers for conversational automatic speech recognition ( ASR ) . We use a fast marginal adaptation ( FMA ) framework to adapt a RNNLM .
A speech therapy game has the potential to make practice fun , may help facilitate increased time and quality of at-home speech therapy , and lead to improved speech . We explore using conversational real-time speech recognition , game methodologies theorized to improve immersion and flow , and user centered approaches to design an immersive interactive speech therapy solution . Our preliminary user evaluation showed that compared to traditional methods , children were more motivated to practice speech using our system .
SND and diarization can then be used to analyze social interactions . In this research privacy preserving audio features has been investigated instead for recording and storage that can respect privacy by minimizing the amount of linguistic information , whereas achieving modern performance in conversational speech processing tasks . Certainly , the main contribution of the proposed system is the achievement of state-of-the-art performances in speech/nonspeech detection and speaker diarization tasks using such features , which we refer to , as privacy-sensitive .
PURPOSE This research note discusses a common misconception in speech science and speech-language pathology textbooks that rib torque ( i.e. , rotational stress ) assists resting tidal expiration and conversational speech production . METHOD The nature of this misconception is considered .
document : Achieving Human Parity in Conversational Speech Recognition Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s . In this paper , we measure the human error rate on the widely used NIST 2000 test set , and find that our latest automated system has reached human parity .
In the meantime , ensemble learning has become commonly used in several neural models , to improve robustness by reducing bias and variance . In this paper , we use ensembles of models extensively , as well as improvements to individual component models , to to advance the state - of - the - art in conversational telephone speech recognition ( CTS ) , which has been a benchmark speech recognition task since the 1990s . The main features of this system are : An ensemble of two fundamental acoustic model architectures , convolutional neural nets ( CNNs ) and long - short - term memory nets ( LSTMs ) , with multiple variants of each An attention mechanism in the LACE CNN which differentially weights distant context Lattice - free MMI training The use of i - vector based adaptation in all models Language model ( LM ) rescoring with multiple , recurrent neural net LMs running in both forward and reverse direction Confusion network system combination coupled with search for best system subset , as necessitated by the large number of candidate systems .
In this paper , we experiment with the recently introduced subword regularization technique [ 1 ] in the context of end-to-end automatic speech recognition ( ASR ) . We present results from both attention-based and CTC-based ASR systems on two common benchmark datasets , the 80 hour Wall Street Journal corpus and 1,000 hour Librispeech corpus .
Experiments on the IEMOCAP dataset demonstrate that the auxiliary learning tasks improve emotion classification accuracy compared to a baseline supervised classifier . Further , we demonstrate that the proposed learning approach can be used for the end-to-end speech emotion recognition , as its applicable for models that operate on frame-level inputs .
To address this problem , we propose to augment a natural speech dataset with synthetic speech . We train very large end-to-end neural speech recognition models using the LibriSpeech dataset augmented with synthetic speech . These new models achieve state of the art Word Error Rate ( WER ) for character-level based models without an external language model .
Recently , many emotion researchers trend the direction to automatic emotion recognition from the raw signal , the motivation behind this is that neural network can learn representation and find the final result automatically . This work focuses on categorization and reviews on the current progress on end-to-end speech emotion recognition problems . In this survey , we discuss the requirement of the network model , process procedures and current achiements .
The acoustic - level predictions are later embedded in an HMM - based framework , that also integrates phone - transitions , lexicon , and language model information to retrieve the final sequence of words . An emerging alternative is end - to - end speech recognition , that aims to drastically simplify the current ASR pipeline by using fully discriminative systems that learn everything from data without ( ideally ) any additional human effort . Popular end - to - end techniques are attention models and Connectionist Temporal Classification ( CTC ) .
document : Quaternion Convolutional Neural Networks for End - to - End Automatic Speech Recognition Recently , the connectionist temporal classification ( CTC ) model coupled with recurrent ( RNN ) or convolutional neural networks ( CNN ) , made it easier to train speech recognition systems in an end - to - end fashion .
Top speech recognition systems rely on sophisticated pipelines composed of multiple algorithms and hand - engineered processing stages . 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 .
For these reasons speech recognition is an interesting testbed for developing new attention - based architectures capable of processing long and noisy inputs . Application of attention - based models to speech recognition is also an important step toward building fully end - to - end trainable speech recognition systems , which is an active area of research . The dominant approach is still based on hybrid systems consisting of a deep neural acoustic model , a triphone HMM model and an n - gram language model .
We show that confidence measures estimated from local posterior probabilities can serve as objective functions for training ANNs in hybrid HMM based speech recognition systems . This leads to a segment-level training paradigm that overcomes the limitation of frame-level updates ignoring the sequence structure in speech .
However , only a few methods rely on tuning the connection weights in trained DNNs directly to optimize system performance since it is very prone to over-fitting especially when some class labels are missing in the adaptation data . In this paper , we propose a new speaker adaptation method for the hybrid NN/HMM speech recognition model based on singular value decomposition ( SVD ) . We apply SVD on the weight matrices in trained DNNs and then tune rectangular diagonal matrices with the adaptation data .
This paper presents a method to improve the recognition rate of hybrid connectionist/HMM speech recognition systems . At the same time this approach allows the easy introduction of context dependent models in the hybrid framework .
This paper presents the results of a large number of experiments performed on the Hiwire cockpit database with a hybrid HMM-ANN speech recognition model . The Hiwire database is a noisy and non-native English speech corpus for cockpit communication .
A recent development in the hybrid HMM/ANN speech recognition paradigm is the use of several subword classifiers , each of which provides different information about the speech signal . Although the combining methods have obtained promising results , the combination strategies so far proposed have been relatively simple .
In the present study , we address this issue by investigating variants of large receptive field CNNs ( LRF-CNNs ) which include deeply recursive networks , dilated convolutional neural networks , and stacked hourglass networks . To compare the efficacy of the aforementioned architectures with the standard CNN for Wall Street Journal ( WSJ ) corpus , we use a hybrid DNN-HMM based speech recognition system . We extend the study to evaluate the system performances for distant speech simulated using realistic room impulse responses ( RIRs ) .
A previously proposed and very promising boosting algorithm is AdaBoost . In this paper we investigate if AdaBoost can be used to improve a hybrid HMM/neural network continuous speech recognizer . Boosting significantly improves the word error rate from 6.3 % to 5.3 % on a test set of the OGI Numbers 95 corpus , a medium size continuous numbers recognition task .
The aim of this work is to develop a practical framework , which extends the classical Hidden Markov Models ( HMM ) for continuous speech recognition based on the Maximum Entropy ( MaxEnt ) principle . The MaxEnt models can estimate the posterior probabilities directly as with Hybrid NN/HMM connectionist speech recognition systems . In particular , a new acoustic modelling based on discriminative MaxEnt models is formulated and is being developed to replace the generative Gaussian Mixture Models ( GMM ) commonly used to model acoustic variability .
As illustrated in fig : brnn , a BRNN computes the forward hidden sequence , the backward hidden sequence and the output sequence by iterating the backward layer from to , the forward layer from to and then updating the output layer : Combing BRNNs with LSTM gives bidirectional LSTM , which can access long - range context in both input directions . A crucial element of the recent success of hybrid HMM - neural network systems is the use of deep architectures , which are able to build up progressively higher level representations of acoustic data . Deep RNNs can be created by stacking multiple RNN hidden layers on top of each other , with the output sequence of one layer forming the input sequence for the next .
subsection : Very deep convolutional networks Very deep CNNs with small kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN - HMM speech recognition systems . Results were provided after cross - entropy training on the 300 hours switchboard - 1 dataset in , and results from sequence training on both switchboard - 1 and the 2000 hours switchboard + Fisher dataset are in .
Posterior‐based attributes in machine recognition of speech. Semantic-Physically Conflicting Speech Perception and Human Cognitive Principle Inspired ASR System Design Self-Adaptive DNN for Improving Spoken Language Proficiency Assessment Enhancement of adaptive de-correlation filtering separation model for robust speech recognition Optimizational Scheme for Constructing Anonymous Spatial Region under Continuous LBS Queries Streaming Automatic Speech Recognition with the Transformer Model Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System Speech recognition with speech synthesis models by marginalising over decision tree leaves Recuperating spectral features using glottal information and its application to speaker recognition Better Evaluation of ASR in Speech Translation Context Using Word Embeddings Automatic speech recognition using audio visual cues Towards spoken clinical-question answering: evaluating and adapting automatic speech-recognition systems for spoken clinical questions Kanban at an Insurance Company (Are You Sure?) The 2001 GMTK-based SPINE ASR system Generalized driven decoding for speech recognition system combination Electric wheel hub motor testing stand Spoken Term Detection based on Phoneme Error Pattern Training using DNN The Relative Distance Vector Neural Network (RDVNN) Model: A Hybrid Approach to Speech Recognition Generalizing prosodic prediction of speech recognition errors Android-based automatic speech recognitionfront end to access Web APIs Deep Speech: Scaling up end-to-end speech recognition Global-Locally Self-Attentive Dialogue State Tracker The IBM 2016 English Conversational Telephone Speech Recognition System Very deep multilingual convolutional neural networks for LVCSR Light Gated Recurrent Units for Speech Recognition A phoneme recognition framework based on auditory spectro-temporal receptive fields Design and analysis of a German telephone speech database for phoneme based training Incorporating Belief Function in SVM for Phoneme Recognition Vocabulary optimization process using similar phoneme recognition and feature extraction On-device mobile speech recognition A robust phonetic digit recognition system for digits spoken in american english and arabic Phrase speech recognition of large vocabulary using feature in articulatory domain On developing new text and audio corpora and speech recognition tools for the turkish language Использование вейвлет-преобразования для построения моделей фонем русского языка Attention-Based Models for Speech Recognition Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition Speech recognition with deep recurrent neural networks Combining acoustic and articulatory feature information for robust speech recognition Modular Synthesis of Disfluencies for Conversational Speech Systems F0 control characterization by perceptual impressions on speaking attitudes using multiple dimensional scaling analysis Belt Up: Investigating the impact of in-vehicular conversation on driving performance Extracting descriptive noun phrases from conversational speech Recurrent Neural Network Language Model Adaptation for Conversational Speech Recognition Designing Towards Maximum Motivation and Engagement in an Interactive Speech Therapy Game An Efficient Speaker Diarization using Privacy Preserving Audio Features Based of Speech/Non Speech Detection Rib torque does not assist resting tidal expiration or most conversational speech expiration. Achieving Human Parity in Conversational Speech Recognition The microsoft 2016 conversational speech recognition system Subword Regularization and Beam Search Decoding for End-to-end Automatic Speech Recognition Improving Emotion Classification through Variational Inference of Latent Variables Training Neural Speech Recognition Systems with Synthetic Speech Augmentation A Survey on Automatic Emotion Recognition Using Audio Big Data and Deep Learning Architectures Light Gated Recurrent Units for Speech Recognition Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition Deep Speech: Scaling up end-to-end speech recognition Attention-Based Models for Speech Recognition Segment-level Training of ANNs Based on Acoustic Confidence Measures for Hybrid HMM/ANN Speech Recognition Speaker Adaptation of Hybrid NN/HMM Model for Speech Recognition Based on Singular Value Decomposition Tied posteriors: an approach for effective introduction of context dependency in hybrid NN/HMM LVCSR Experiments on hiwire database using denoising and adaptation with a hybrid HMM-ANN model Dynamic classifier combination using utterance-level confidence values Analyzing Large Receptive Field Convolutional Networks for Distant Speech Recognition Using boosting to improve a hybrid HMM/neural network speech recognizer A hybrid Maxent/HMM based ASR system Speech recognition with deep recurrent neural networks The IBM 2016 English Conversational Telephone Speech Recognition System
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