This book presents the state of the art in recognition in the presence of uncertainty , offering examples that utilize uncertainty information for noise robustness , reverberation robustness , simultaneous recognition of multiple speech signals , and audiovisual speech recognition . The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition , professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch , and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field . The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models . In this paper we present an insight into the use of spectral masking techniques in time-frequency domain , as a preprocessing step for the speech signal recognition . Speech recognition systems have their performance negatively affected in noisy environments or in the presence of other speech signals . Such a classification can also help in the optimal therapy selection and in determining rehabilitation recommendations . The methods of transformation , analysis , classification and speech signal recognition have been known apparently for many years , as with no difficulty , many writings discussing these concepts and presenting results concerning both basic examinations results and many application works , can be found . The problem , presented in this article , is strongly different from the majority of works , which were published by other authors , as most of well known works , concerning the speech signal analysis ( etc . ) , are directed to understand the content of the statement ( automatic speech understanding , a speech-writing conversion , steering of the devices by speech signal ) , or relatively by determining the identity of the speaker ( automatic identification or authentification of the speaker ) . We then compare the performance of our novel features with standardised brute-force and bag-of-audio-words ( BoAW ) acoustic feature representations for 2- and 5-class speech-based emotion recognition in clean , noisy and denoised conditions . The presented results show that image-based approaches are a promising avenue of research for speech-based recognition tasks . Key results indicate that deep-spectrum features are comparable in performance with the other tested acoustic feature representations in matched for noise type train-test conditions ; however , the BoAW paradigm is better suited to cross-noise-type train-test conditions . Although speaker recognition is currently not as robust as other biometrics such as finger prints and retinal scans , speech holds great promise . Speech based recognition permits remote access . Speech is very important in a country like India with a large population with low levels of literacy and education . A kind of system and method based on vehicle , it is for receiving phonetic entry and determining whether that the resource using vehicle-mounted resource or outside vehicle is to perform speech recognition analysis . 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 . Modern speech recognizers rely on three core components : an acoustic model , a language model , and a pronunciation lexicon . In order to expand speech recognition capability to lowresource languages and domains , techniques to peel away the expert knowledge required to craft these three components have been growing in popularity . In this paper , we present a method for automatically learning a weighted pronunciation lexicon in a data-driven fashion without assuming the existence of any phonetic lexicon whatsoever . This chapter introduces the history and development of speech recognition , addresses its role in the curriculum , outlines related national and state standards , describes appropriate instructional strategies , and discusses the assessment of student achievement in speech recognition classes . The History and Development of Speech Recognition Speech recognition is a technology that has evolved over many years , beginning in 1936 when Bell Laboratories began conducting research on automatic speech recognition and transcription ( " History of Speech , " n.d . ) . In 1950 Bell Laboratories developed the first technology that could recognize spoken numbers ( " The History of Voice Recognition , " 2004 ) , and in 1964 IBM demonstrated its " shoe box recognizer " for spoken digits at the World 's Fair ( IBM Research , 2007 ) . II . SPEECH RECOGNITION Speech recognition is the process of converting an acoustic signal , captured by a microphone or a telephone , to a set of words . The recognized words can be the final result , as for applications such as command & control , data entry , and A. M. Othman was with the SE Department , Faculty of Information Technology , ASU/Jordan , He is now with MIS Department , the Amman Arab University for Graduate Studies , AAUJORDAN(corresponding phone : + 962 - 796184806 ; fax : 962 - 6 - 55 16103 ; e-mail : akram.othman@aau.edu.jo ) . Signal processing apparatus according to an aspect of the present invention includes a speech recognizer to recognize speech ; Voice recognition learning unit for learning the subject of the speech recognition and voice ; Input control unit the speech recognition to recognize the voice that is the destination of the speech recognition of the speech , and if successful the speech recognition control unit the speech recognition learning , and when there is a failure in the speech recognition , so as to learn the input speech is includes performing a specific operation corresponding to the recognized speech control , through the study of the final relationship of the speech command and the previous command is successful as by storing the previous command has failed , unlike the intent when the to , prior to study at the time of subsequent study who can successfully take advantage of voice commands to fail . Voice recognition , failure , learning was the first to propose a differentiable attention mechanism for the general problem of handwritten text synthesis , although his approach assumed a monotonic alignment between the input and output sequences . Later , introduced a more general attention model that does not assume a monotonic alignment , and applied it to machine translation , and applied the same model to speech recognition . used a convolutional neural network to encode a variable - sized input sentence into a vector of a fixed dimension and used a RNN to produce the output sentence . section : INTRODUCTION Neural network models have recently made tremendous progress in a variety of NLP applications such as speech recognition [ reference ] , sentiment analysis [ reference ] ) , text summarization [ reference ] , and machine translation [ reference ] . Despite the overwhelming success achieved by recurrent neural networks in modeling long range dependencies between words , current recurrent neural network language models ( RNNLM ) are based on the conventional classification framework , which has two major drawbacks : First , there is no assumed metric on the output classes , whereas there is evidence suggesting that learning is improved when one can define a natural metric on the output space [ reference ] . The embedded inference is fast and accurate while enforcing privacy by design , as no personal user data is ever collected . Focusing on Automatic Speech Recognition and Natural Language Understanding , we detail our approach to training high - performance Machine Learning models that are small enough to run in real - time on small devices . Additionally , we describe a data generation procedure that provides sufficient , high - quality training data without compromising user privacy . The development of robust ASR has been recently fostered by the great success of some international challenges such as CHiME , REVERB and ASpIRE , which were also extremely useful to establish common evaluation frameworks among researchers . Currently , the dominant approach to automatic speech recognition relies on a combination of a discriminative DNN and a generative Hidden Markov Model ( HMM ) . The DNN is normally employed for acoustic modeling purposes to predict context - dependent phone targets . RNNs , indeed , can potentially capture temporal information in a very dynamic fashion , allowing the network to freely decide the amount of contextual information to use for each time step . Several works have already highlighted the effectiveness of RNNs in various speech processing tasks , such as speech recognition , speech enhancement , speech separation as well as speech activity detection . Training RNNs , however , can be complicated by vanishing and exploding gradients , that might impair learning long - term dependencies . We also observe that GRUs tend to slightly outperform the LSTM model . As expected , M - GRU ( i.e. , the architecture without reset gate ) achieves a performance very similar to that obtained with standard GRUs , further supporting our speculation on the redundant role played by the reset gate in a speech recognition application . The last row reports the performance achieved with the proposed model , in which , besides removing the reset gate , ReLU activations are used . The Li - GRU , in fact , not only yields a better recognition performance , but also reduces the computational complexity , with a reduction of more than 30 % of the training time over a standard GRU . Future efforts will be focused on extending this work to other speech - based tasks , such as speech enhancement and speech separation as well as to explore the use of Li - GRU in other possible fields . section : Acknowledgments document : Natural TTS Synthesis By Conditioning WaveNet On Mel Spectrogram Predictions This paper describes Tacotron 2 , a neural network architecture for speech synthesis directly from text . The system is composed of a recurrent sequence - to - sequence feature prediction network that maps character embeddings to mel - scale spectrograms , followed by a modified WaveNet model acting as a vocoder to synthesize time - domain waveforms from those spectrograms . In particular , the variational autoencoder algorithm kingma2013auto , rezende2014stochastic simultaneously learns a generative network , that maps gaussian latent variables to samples , and a matched approximate inference network that maps samples to a semantically meaningful latent representation , by exploiting the reparametrization trick williams1992simple . Its success in leveraging recent advances in backpropagation rumelhart1988learning , lecun2012efficient in deep neural networks resulted in its adoption for several applications ranging from speech synthesis chung2015recurrent to language modeling bowman2015generating . Still , the approximation in the inference process limits its ability to learn high dimensional deep representations , motivating recent work in improving approximate inference maaloe2016auxiliary , rezende2015variational , salimans2014markov , tran2015variational , burda2015importance , DBLP : conf / icml / Sohl - DicksteinW15 , kingma2016improving . In hard parameter sharing , a subset of the parameters is shared between tasks while other parameters are task - specific . In soft parameter sharing , all parameters are task - specific but they are jointly constrained via Bayesian priors [ reference ][ reference ] or a joint dictionary [ reference ][ reference ][ reference ][ reference ] . We focus on hard parameter sharing with gradient - based optimization , following the success of deep MTL in computer vision [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] , natural language processing [ reference ][ reference ][ reference ][ reference ][ reference ] , speech processing [ reference ][ reference ][ reference ] , and even seemingly unrelated domains over multiple modalities [ reference ] . [ reference ] theoretically analyze the MTL problem as interaction between individual learners and a meta - algorithm . Each learner is responsible for one task and a meta - algorithm decides how the shared parameters are updated . n’t require an initial alignment of the training data which is typically done with a GMM - based baseline model . The above points beg the question whether , in this age of readily available NN toolkits , speech recognition expertise is still necessary or whether one can simply point a neural net to the audio and transcripts , let it train , and obtain a good acoustic model . While it is true that , as the amount of training data increases , the need for human ASR expertise is lessened , at the moment the performance of end - to - end systems ultimately remains inferior to that of more traditional , i.e. HMM and decision tree - based , approaches . The downstream tracker then combines the semantics extracted by the SLU with previous dialogue context in order to estimate the current dialogue state . 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 . Not surprisingly , and for both frontal and profile views , we conclude that the central mouth patches are the most informative , but less so than the holistic features of the entire ROI . Nevertheless , fusion of holistic and the best patch based features further improves visual speech recognition performance , compared to either feature set alone . Finally , we discuss scenarios where the patch based approach may be preferable to holistic features . Appearance-based feature extraction constitutes the dominant approach for visual speech representation in a variety of problems , such as automatic speechreading , visual speech detection , and others . To obtain the necessary visual features , typically a rectangular region-of-interest ( ROI ) containing the speaker ’s mouth is first extracted , followed , most commonly , by a discrete cosine transform ( DCT ) of the ROI pixel values and a feature selection step . The visual information from television recordings is processed in our system too . The modules for visual signal segmentation , for face detection and identification , and for visual speech detection have been added to the transcription system . Indexed recognized visual information is saved to the database together with the information from acoustic signal and it is included in the searching web-system . In the explicit task ( a ) , each stimulus is classified directly as a synthetic or real image-sequence by detecting a possible difference between the synthetic and the real image-sequences . The implicit perceptual discrimination task ( b ) consists of a comparison between visual recognition of speech of real and synthetic image-sequences . Our results suggest that implicit perceptual discrimination is a more sensitive method for discrimination between synthetic and real image-sequences than explicit perceptual discrimination . Visual recognition of speech using the lip movement is called Lip-reading . Recent developments in this nascent field use different neural networks as feature extractors which serve as input to a model which can map the temporal relationship and classify . In this paper , we will focus on investigating the effectiveness of this technique for the related speaker verification application . By investigating the speaker verification ability of each stage of the cascade we will demonstrate that the same steps taken to reduce static speaker and environmental information for the visual speech recognition application also provide similar improvements for visual speaker recognition . A further study is conducted comparing synchronous HMM ( SHMM ) based fusion of CAB visual features and traditional perceptual linear predictive ( PLP ) acoustic features to show that higher complexity inherit in the SHMM approach does not appear to provide any improvement in the final audio-visual speaker verification system over simpler utterance level score fusion . To improve the accuracy of audio-visual speaker identification , we propose a new approach , which achieves an optimal combination of the different modalities on the score level . We use the i-vector method for the acoustics and the local binary pattern ( LBP ) for the visual speaker recognition . Regarding the input data of both modalities , multiple confidence measures are utilized to calculate an optimal weight for the fusion . Recently , lip image segmentation has attracted much attention because the lip image segmentation result provides much visual information for recognition of visual speech . In this paper , a new approach for lip segmentation based on deep neural network is presented . Our proposed algorithm is particularly designed for multi-class problem . Experimental results on two real world classification problems arising in visual and speech recognition demonstrate the effectiveness of our algorithm . In Smart Factories , automated guided vehicles ( AGVs ) accomplish heterogeneous tasks as moving objects , restoring connectivity , or performing different manufacturing activities into production lines . These kinds of devices combine several capabilities , as artificial intelligence ( visual and speech recognition , automatic fault detecting , proactive behavior ) and mobility , into the so-called “ mobile intelligence . ” A typical scenario is represented by a workshop with a large number of mobile intelligent devices with associated agents , mutually interacting on their behalf . We have studied Tulipse1 database , AV Database and CUAVE Database on the basis of these different databases we have created our own database . This is useful for all researchers those are working HCI domain particularly Visual Speech and Speaker Recognition . Visual information is always combined as a complementary source to enhance the understanding of what the speaker is talking about , especially in a noisy environment . This paper researches on different lip features for visual speech and speaker recognition , and their robustness to different uttering habits is conducted in-depth analysis . Five feature candidates extracted from lip shape are tested and compared on a multispeaker visual speech recognition task of isolated English digits ( 0~9 ) . To date , multimodal speech recognition systems based on the processing of audio and video signals show significantly better results than their unimodal counterparts . In general , researchers divide the solution of the audio – visual speech recognition problem into two parts . First , in extracting the most informative features from each modality and second , in the most successful way of fusion both modalities . The proposed VSR approach is termed " visual words " . The visual words approach consists of two main parts , 1 ) Feature extraction/selection , and 2 ) Visual speech feature recognition . After localizing face and lips , several visual features for the lips where extracted . In the case of the image domain ( video sequences ) we create spatio temporal templates by stacking the video frames , and the detection is performed on these templates . In order to recognize the motion of features in a video sequence , the spatial locations of the features are modulated in time , thus creating a one-dimensional vector which re resents the event The following applications of anti-sequences are presented 1 ) Detection of an object under 3D rotations in a video sequence simulated from the COIL database , 2 ) Visual speech recognition of spoken words , and 3 ) Recognition of symbols sketched with a laser pointer . The resulting detection algorithm is very fast , and is robust enough to work on small images . As designed , CNNs are equipped with basic multi - scale feature representation ability since the input information follows a coarse - to - fine fashion . The AlexNet stacks filters sequentially and achieves significant performance gain over traditional methods for visual recognition . However , due to the limited network depth and kernel size of filters , the AlexNet has only a relatively small receptive field . In order to promote English teaching quality , English speech recognition has been attracted more and more attention . In this paper , we aim to propose a novel English speech recognition approach based on Hidden Markov model . We have developed the CU voice browser based on OpenVXI 2.0 . We have also integrated the voice browser with the OpenSpeech Recognizer ( for English speech recognition ) , CU RSBB ( for Chinese speech recognition ) , Speechify ( for English speech synthesis ) and CU VOCAL ( for Chinese speech synthesis ) in order to support bilingual voice browsing . The CU voice browser includes an attribute for identifying the appropriate language for speech input/output , thereby invoking the appropriate speech engine for recognition/synthesis . This allows a transform which is discriminative in nature to be indirectly estimated , while only requiring an ML speaker-specific transform to be found during recognition . The DMT technique is evaluated on an English conversational telephone speech task . Experiments showed that using DMT in unsupervised adaptation led to significant gains over both standard ML and discriminatively trained transforms . As DMTs are estimated during training , they are not affected by errors in the supervision hypothesis . The proposed method was evaluated on an English conversational telephone speech task . It was found to significantly outperform the standard discriminative SAT schemes . In this paper , a phonemic segmentation system is constructed which will be useful in practice as a preprocessing for English continuous speech recognition . the usefulness of the system is demonstrated . The results of paired-sample t-test indicated that there was a significant difference between pre and post oral proficiency tests t ( 26 ) = -14.83 , p<0.001 . Moreover , it was found that students ’ involvement and willingness to participate in the English conversational tasks and their level of commitment and English speaking performance have increased gradually . In light of the findings , the study recommended encouraging teachers to employ modern technologies in their teaching strategies , particularly FCM . To address these problems , the use of discriminative mapping transforms is also described . The proposed framework is evaluated on an English conversational speech task . Over the past decade , there has been good progress on English conversational telephone speech ( CTS ) recognition , built on the Switchboard and Fisher corpora . In this paper , we present our efforts on extending language-independent technologies into Mandarin CTS , as well as addressing language-dependent issues such as tone . This paper shows the implementation process of speech recognition on a low-end computational device . Development of English-language speech recognition that has many datasets become a good point for starting . The model that used results from pre-trained model that provide by each version of deepspeech , without change of the model that already released , furthermore the benefit of using raspberry pi as a media end-to-end speech recognition device become a good thing , user can change and modify of the speech recognition , and also deepspeech can be standalone device without need continuously internet connection to process speech recognition , and even this paper show the power of Tensorflow Lite can make a significant difference on inference by deepspeech rather than using Tensorflow non-Lite . In previous work we have defined a pseudosyllable unit for an English read speech recognition task . In this study we investigate the robustness of extraction of the pseudosyllable units and investigate how such units can be integrated into speech recognition systems . Then , we test the English speech recognition accuracy of four different noisy environments . Experimental results prove that the proposed approach can effectively enhance accuracy of English speech recognition process . Second , we apply multiscale convolution from several previous attention vectors to the current decoder state . We utilized our proposed framework for sequence-to-sequence speech recognition and text-to-speech systems . The results reveal that our proposed extension can improve performance significantly compared to a standard attention baseline . This paper is concerned with isolated speech sequence recognition problem . The dynamic time-warping method is applied for implementation of the very fast real-time speech sequence recognition . An advanced version of the basic algorithm is incorporated in the AREX v1.0 application for the real-time isolated speech sequence recognition . The systems considered in this paper are based on connectionist models , or artificial neural networks , sometimes combined with statistical techniques for recognition of sequences of patterns , stressing the integration of prior knowledge and learning . Different architectures for sequence and speech recognition are reviewed , including recurrent networks as well as hybrid systems involving hidden Markov models . A low-power and real-time hidden Markov model ( HMM ) accelerator is proposed for gesture user interfaces on wearable smart devices . HMM algorithm is widely used for sequence recognitions such as speech recognition and gesture recognition due to its best-in-class recognition accuracy . However , the HMM algorithm incorporates high computational complexity and requires massive memory bandwidth for sequence matches . This paper proposes a forward attention method for the sequence-to-sequence acoustic modeling of speech synthesis . This method is motivated by the nature of the monotonic alignment from phone sequences to acoustic sequences . The underlying genetic architecture is complex , involving a range of molecular mechanisms . For example , rare protein-coding mutations of the FOXP2 transcription factor cause severe problems with sequencing of speech sounds , while common genetic risk variants of small effect size in genes like CNTNAP2 , ATP2C2 and CMIP are associated with typical forms of language impairment . In this article , we describe how investigations of these and other candidate genes , in humans , animals and cellular models , are unravelling the connections between genes and cognition . For applications such as speech recognition , where we deal with ordered sequences , it is desirable to impose a left-to-right structure on the model to improve its ability to model the sequential nature of the speech signal . In this paper , we introduce three enhancements to HDP-HMM : ( 1 ) a left-to-right structure : needed for sequential decoding of speech , ( 2 ) non-emitting initial and final states : required for modeling finite length sequences , ( 3 ) HDP mixture emissions : allows sharing of data across states . The latter is particularly important for speech recognition because Gaussian mixture models have been very effective at modeling speaker variability . For this reason it seems unsuitable as an introduction to neural network techniques for cognitive scientists , psychologists or linguists , although the book might suit those with a strong mathematical or computing background . However , Bengio provides clear descriptions of numerous advanced techniques , the underlying theory and mathematics , and examples of applications in speech and sequence processing . As such , his work may be of considerable'value to those interested in machine learning seeking to apply similar techniques within a psychological framework , or working with raw speech rather than some idealization . Procedure speech recognition sequences words composed of several words from a given , in which are provided a first recognition method and a second recognition process vocabulary , wherein the first recognition method and the second recognition method are applied to separate segments of a sequence of words to be recognized , wherein the first procedure a recognition procedure bigram with unique syntax integrated as the second recognition process of a recognition procedure applies statistical evaluation of the sequences of words it is applied , and Upon a change of bigram recognition procedure with the second integrated syntax univocal recognition procedure with statistical assessment of word sequences the last two words of the treated segment are grouped according to the first procedure for one nonword that is a bigram recognition method . Correctly ordering a sequence of speech sounds is a crucial aspect of speech production . Although studies have yielded a rich body of data on the neural substrates of visuomotor sequencing and sequence learning , research on brain regions and their functions involving speech sequence production has n’t attracted much attention until recently . Previous functional MRI studies manipulating the complexity of sequences at the phonemic , syllabic , and suprasyllabic levels have revealed a network of motor-related cortical and sub-cortical speech regions .
This book presents the state of the art in recognition in the presence of uncertainty , offering examples that utilize uncertainty information for noise robustness , reverberation robustness , simultaneous recognition of multiple speech signals , and audiovisual speech recognition . The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition , professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch , and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field . The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models .
In this paper we present an insight into the use of spectral masking techniques in time-frequency domain , as a preprocessing step for the speech signal recognition . Speech recognition systems have their performance negatively affected in noisy environments or in the presence of other speech signals .
Such a classification can also help in the optimal therapy selection and in determining rehabilitation recommendations . The methods of transformation , analysis , classification and speech signal recognition have been known apparently for many years , as with no difficulty , many writings discussing these concepts and presenting results concerning both basic examinations results and many application works , can be found . The problem , presented in this article , is strongly different from the majority of works , which were published by other authors , as most of well known works , concerning the speech signal analysis ( etc . ) , are directed to understand the content of the statement ( automatic speech understanding , a speech-writing conversion , steering of the devices by speech signal ) , or relatively by determining the identity of the speaker ( automatic identification or authentification of the speaker ) .
We then compare the performance of our novel features with standardised brute-force and bag-of-audio-words ( BoAW ) acoustic feature representations for 2- and 5-class speech-based emotion recognition in clean , noisy and denoised conditions . The presented results show that image-based approaches are a promising avenue of research for speech-based recognition tasks . Key results indicate that deep-spectrum features are comparable in performance with the other tested acoustic feature representations in matched for noise type train-test conditions ; however , the BoAW paradigm is better suited to cross-noise-type train-test conditions .
Although speaker recognition is currently not as robust as other biometrics such as finger prints and retinal scans , speech holds great promise . Speech based recognition permits remote access . Speech is very important in a country like India with a large population with low levels of literacy and education .
A kind of system and method based on vehicle , it is for receiving phonetic entry and determining whether that the resource using vehicle-mounted resource or outside vehicle is to perform speech recognition analysis .
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 .
Modern speech recognizers rely on three core components : an acoustic model , a language model , and a pronunciation lexicon . In order to expand speech recognition capability to lowresource languages and domains , techniques to peel away the expert knowledge required to craft these three components have been growing in popularity . In this paper , we present a method for automatically learning a weighted pronunciation lexicon in a data-driven fashion without assuming the existence of any phonetic lexicon whatsoever .
This chapter introduces the history and development of speech recognition , addresses its role in the curriculum , outlines related national and state standards , describes appropriate instructional strategies , and discusses the assessment of student achievement in speech recognition classes . The History and Development of Speech Recognition Speech recognition is a technology that has evolved over many years , beginning in 1936 when Bell Laboratories began conducting research on automatic speech recognition and transcription ( " History of Speech , " n.d . ) . In 1950 Bell Laboratories developed the first technology that could recognize spoken numbers ( " The History of Voice Recognition , " 2004 ) , and in 1964 IBM demonstrated its " shoe box recognizer " for spoken digits at the World 's Fair ( IBM Research , 2007 ) .
II . SPEECH RECOGNITION Speech recognition is the process of converting an acoustic signal , captured by a microphone or a telephone , to a set of words . The recognized words can be the final result , as for applications such as command & control , data entry , and A. M. Othman was with the SE Department , Faculty of Information Technology , ASU/Jordan , He is now with MIS Department , the Amman Arab University for Graduate Studies , AAUJORDAN(corresponding phone : + 962 - 796184806 ; fax : 962 - 6 - 55 16103 ; e-mail : akram.othman@aau.edu.jo ) .
Signal processing apparatus according to an aspect of the present invention includes a speech recognizer to recognize speech ; Voice recognition learning unit for learning the subject of the speech recognition and voice ; Input control unit the speech recognition to recognize the voice that is the destination of the speech recognition of the speech , and if successful the speech recognition control unit the speech recognition learning , and when there is a failure in the speech recognition , so as to learn the input speech is includes performing a specific operation corresponding to the recognized speech control , through the study of the final relationship of the speech command and the previous command is successful as by storing the previous command has failed , unlike the intent when the to , prior to study at the time of subsequent study who can successfully take advantage of voice commands to fail . Voice recognition , failure , learning
was the first to propose a differentiable attention mechanism for the general problem of handwritten text synthesis , although his approach assumed a monotonic alignment between the input and output sequences . Later , introduced a more general attention model that does not assume a monotonic alignment , and applied it to machine translation , and applied the same model to speech recognition . used a convolutional neural network to encode a variable - sized input sentence into a vector of a fixed dimension and used a RNN to produce the output sentence .
section : INTRODUCTION Neural network models have recently made tremendous progress in a variety of NLP applications such as speech recognition [ reference ] , sentiment analysis [ reference ] ) , text summarization [ reference ] , and machine translation [ reference ] . Despite the overwhelming success achieved by recurrent neural networks in modeling long range dependencies between words , current recurrent neural network language models ( RNNLM ) are based on the conventional classification framework , which has two major drawbacks : First , there is no assumed metric on the output classes , whereas there is evidence suggesting that learning is improved when one can define a natural metric on the output space [ reference ] .
The embedded inference is fast and accurate while enforcing privacy by design , as no personal user data is ever collected . Focusing on Automatic Speech Recognition and Natural Language Understanding , we detail our approach to training high - performance Machine Learning models that are small enough to run in real - time on small devices . Additionally , we describe a data generation procedure that provides sufficient , high - quality training data without compromising user privacy .
The development of robust ASR has been recently fostered by the great success of some international challenges such as CHiME , REVERB and ASpIRE , which were also extremely useful to establish common evaluation frameworks among researchers . Currently , the dominant approach to automatic speech recognition relies on a combination of a discriminative DNN and a generative Hidden Markov Model ( HMM ) . The DNN is normally employed for acoustic modeling purposes to predict context - dependent phone targets .
RNNs , indeed , can potentially capture temporal information in a very dynamic fashion , allowing the network to freely decide the amount of contextual information to use for each time step . Several works have already highlighted the effectiveness of RNNs in various speech processing tasks , such as speech recognition , speech enhancement , speech separation as well as speech activity detection . Training RNNs , however , can be complicated by vanishing and exploding gradients , that might impair learning long - term dependencies .
We also observe that GRUs tend to slightly outperform the LSTM model . As expected , M - GRU ( i.e. , the architecture without reset gate ) achieves a performance very similar to that obtained with standard GRUs , further supporting our speculation on the redundant role played by the reset gate in a speech recognition application . The last row reports the performance achieved with the proposed model , in which , besides removing the reset gate , ReLU activations are used .
The Li - GRU , in fact , not only yields a better recognition performance , but also reduces the computational complexity , with a reduction of more than 30 % of the training time over a standard GRU . Future efforts will be focused on extending this work to other speech - based tasks , such as speech enhancement and speech separation as well as to explore the use of Li - GRU in other possible fields . section : Acknowledgments
document : Natural TTS Synthesis By Conditioning WaveNet On Mel Spectrogram Predictions This paper describes Tacotron 2 , a neural network architecture for speech synthesis directly from text . The system is composed of a recurrent sequence - to - sequence feature prediction network that maps character embeddings to mel - scale spectrograms , followed by a modified WaveNet model acting as a vocoder to synthesize time - domain waveforms from those spectrograms .
In particular , the variational autoencoder algorithm kingma2013auto , rezende2014stochastic simultaneously learns a generative network , that maps gaussian latent variables to samples , and a matched approximate inference network that maps samples to a semantically meaningful latent representation , by exploiting the reparametrization trick williams1992simple . Its success in leveraging recent advances in backpropagation rumelhart1988learning , lecun2012efficient in deep neural networks resulted in its adoption for several applications ranging from speech synthesis chung2015recurrent to language modeling bowman2015generating . Still , the approximation in the inference process limits its ability to learn high dimensional deep representations , motivating recent work in improving approximate inference maaloe2016auxiliary , rezende2015variational , salimans2014markov , tran2015variational , burda2015importance , DBLP : conf / icml / Sohl - DicksteinW15 , kingma2016improving .
In hard parameter sharing , a subset of the parameters is shared between tasks while other parameters are task - specific . In soft parameter sharing , all parameters are task - specific but they are jointly constrained via Bayesian priors [ reference ][ reference ] or a joint dictionary [ reference ][ reference ][ reference ][ reference ] . We focus on hard parameter sharing with gradient - based optimization , following the success of deep MTL in computer vision [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] , natural language processing [ reference ][ reference ][ reference ][ reference ][ reference ] , speech processing [ reference ][ reference ][ reference ] , and even seemingly unrelated domains over multiple modalities [ reference ] . [ reference ] theoretically analyze the MTL problem as interaction between individual learners and a meta - algorithm . Each learner is responsible for one task and a meta - algorithm decides how the shared parameters are updated .
n’t require an initial alignment of the training data which is typically done with a GMM - based baseline model . The above points beg the question whether , in this age of readily available NN toolkits , speech recognition expertise is still necessary or whether one can simply point a neural net to the audio and transcripts , let it train , and obtain a good acoustic model . While it is true that , as the amount of training data increases , the need for human ASR expertise is lessened , at the moment the performance of end - to - end systems ultimately remains inferior to that of more traditional , i.e. HMM and decision tree - based , approaches .
The downstream tracker then combines the semantics extracted by the SLU with previous dialogue context in order to estimate the current dialogue state . 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 .
Not surprisingly , and for both frontal and profile views , we conclude that the central mouth patches are the most informative , but less so than the holistic features of the entire ROI . Nevertheless , fusion of holistic and the best patch based features further improves visual speech recognition performance , compared to either feature set alone . Finally , we discuss scenarios where the patch based approach may be preferable to holistic features .
Appearance-based feature extraction constitutes the dominant approach for visual speech representation in a variety of problems , such as automatic speechreading , visual speech detection , and others . To obtain the necessary visual features , typically a rectangular region-of-interest ( ROI ) containing the speaker ’s mouth is first extracted , followed , most commonly , by a discrete cosine transform ( DCT ) of the ROI pixel values and a feature selection step .
The visual information from television recordings is processed in our system too . The modules for visual signal segmentation , for face detection and identification , and for visual speech detection have been added to the transcription system . Indexed recognized visual information is saved to the database together with the information from acoustic signal and it is included in the searching web-system .
In the explicit task ( a ) , each stimulus is classified directly as a synthetic or real image-sequence by detecting a possible difference between the synthetic and the real image-sequences . The implicit perceptual discrimination task ( b ) consists of a comparison between visual recognition of speech of real and synthetic image-sequences . Our results suggest that implicit perceptual discrimination is a more sensitive method for discrimination between synthetic and real image-sequences than explicit perceptual discrimination .
Visual recognition of speech using the lip movement is called Lip-reading . Recent developments in this nascent field use different neural networks as feature extractors which serve as input to a model which can map the temporal relationship and classify .
In this paper , we will focus on investigating the effectiveness of this technique for the related speaker verification application . By investigating the speaker verification ability of each stage of the cascade we will demonstrate that the same steps taken to reduce static speaker and environmental information for the visual speech recognition application also provide similar improvements for visual speaker recognition . A further study is conducted comparing synchronous HMM ( SHMM ) based fusion of CAB visual features and traditional perceptual linear predictive ( PLP ) acoustic features to show that higher complexity inherit in the SHMM approach does not appear to provide any improvement in the final audio-visual speaker verification system over simpler utterance level score fusion .
To improve the accuracy of audio-visual speaker identification , we propose a new approach , which achieves an optimal combination of the different modalities on the score level . We use the i-vector method for the acoustics and the local binary pattern ( LBP ) for the visual speaker recognition . Regarding the input data of both modalities , multiple confidence measures are utilized to calculate an optimal weight for the fusion .
Recently , lip image segmentation has attracted much attention because the lip image segmentation result provides much visual information for recognition of visual speech . In this paper , a new approach for lip segmentation based on deep neural network is presented .
Our proposed algorithm is particularly designed for multi-class problem . Experimental results on two real world classification problems arising in visual and speech recognition demonstrate the effectiveness of our algorithm .
In Smart Factories , automated guided vehicles ( AGVs ) accomplish heterogeneous tasks as moving objects , restoring connectivity , or performing different manufacturing activities into production lines . These kinds of devices combine several capabilities , as artificial intelligence ( visual and speech recognition , automatic fault detecting , proactive behavior ) and mobility , into the so-called “ mobile intelligence . ” A typical scenario is represented by a workshop with a large number of mobile intelligent devices with associated agents , mutually interacting on their behalf .
We have studied Tulipse1 database , AV Database and CUAVE Database on the basis of these different databases we have created our own database . This is useful for all researchers those are working HCI domain particularly Visual Speech and Speaker Recognition .
Visual information is always combined as a complementary source to enhance the understanding of what the speaker is talking about , especially in a noisy environment . This paper researches on different lip features for visual speech and speaker recognition , and their robustness to different uttering habits is conducted in-depth analysis . Five feature candidates extracted from lip shape are tested and compared on a multispeaker visual speech recognition task of isolated English digits ( 0~9 ) .
To date , multimodal speech recognition systems based on the processing of audio and video signals show significantly better results than their unimodal counterparts . In general , researchers divide the solution of the audio – visual speech recognition problem into two parts . First , in extracting the most informative features from each modality and second , in the most successful way of fusion both modalities .
The proposed VSR approach is termed " visual words " . The visual words approach consists of two main parts , 1 ) Feature extraction/selection , and 2 ) Visual speech feature recognition . After localizing face and lips , several visual features for the lips where extracted .
In the case of the image domain ( video sequences ) we create spatio temporal templates by stacking the video frames , and the detection is performed on these templates . In order to recognize the motion of features in a video sequence , the spatial locations of the features are modulated in time , thus creating a one-dimensional vector which re resents the event The following applications of anti-sequences are presented 1 ) Detection of an object under 3D rotations in a video sequence simulated from the COIL database , 2 ) Visual speech recognition of spoken words , and 3 ) Recognition of symbols sketched with a laser pointer . The resulting detection algorithm is very fast , and is robust enough to work on small images .
As designed , CNNs are equipped with basic multi - scale feature representation ability since the input information follows a coarse - to - fine fashion . The AlexNet stacks filters sequentially and achieves significant performance gain over traditional methods for visual recognition . However , due to the limited network depth and kernel size of filters , the AlexNet has only a relatively small receptive field .
In order to promote English teaching quality , English speech recognition has been attracted more and more attention . In this paper , we aim to propose a novel English speech recognition approach based on Hidden Markov model .
We have developed the CU voice browser based on OpenVXI 2.0 . We have also integrated the voice browser with the OpenSpeech Recognizer ( for English speech recognition ) , CU RSBB ( for Chinese speech recognition ) , Speechify ( for English speech synthesis ) and CU VOCAL ( for Chinese speech synthesis ) in order to support bilingual voice browsing . The CU voice browser includes an attribute for identifying the appropriate language for speech input/output , thereby invoking the appropriate speech engine for recognition/synthesis .
This allows a transform which is discriminative in nature to be indirectly estimated , while only requiring an ML speaker-specific transform to be found during recognition . The DMT technique is evaluated on an English conversational telephone speech task . Experiments showed that using DMT in unsupervised adaptation led to significant gains over both standard ML and discriminatively trained transforms .
As DMTs are estimated during training , they are not affected by errors in the supervision hypothesis . The proposed method was evaluated on an English conversational telephone speech task . It was found to significantly outperform the standard discriminative SAT schemes .
In this paper , a phonemic segmentation system is constructed which will be useful in practice as a preprocessing for English continuous speech recognition . the usefulness of the system is demonstrated .
The results of paired-sample t-test indicated that there was a significant difference between pre and post oral proficiency tests t ( 26 ) = -14.83 , p<0.001 . Moreover , it was found that students ’ involvement and willingness to participate in the English conversational tasks and their level of commitment and English speaking performance have increased gradually . In light of the findings , the study recommended encouraging teachers to employ modern technologies in their teaching strategies , particularly FCM .
To address these problems , the use of discriminative mapping transforms is also described . The proposed framework is evaluated on an English conversational speech task .
Over the past decade , there has been good progress on English conversational telephone speech ( CTS ) recognition , built on the Switchboard and Fisher corpora . In this paper , we present our efforts on extending language-independent technologies into Mandarin CTS , as well as addressing language-dependent issues such as tone .
This paper shows the implementation process of speech recognition on a low-end computational device . Development of English-language speech recognition that has many datasets become a good point for starting . The model that used results from pre-trained model that provide by each version of deepspeech , without change of the model that already released , furthermore the benefit of using raspberry pi as a media end-to-end speech recognition device become a good thing , user can change and modify of the speech recognition , and also deepspeech can be standalone device without need continuously internet connection to process speech recognition , and even this paper show the power of Tensorflow Lite can make a significant difference on inference by deepspeech rather than using Tensorflow non-Lite .
In previous work we have defined a pseudosyllable unit for an English read speech recognition task . In this study we investigate the robustness of extraction of the pseudosyllable units and investigate how such units can be integrated into speech recognition systems .
Then , we test the English speech recognition accuracy of four different noisy environments . Experimental results prove that the proposed approach can effectively enhance accuracy of English speech recognition process .
Second , we apply multiscale convolution from several previous attention vectors to the current decoder state . We utilized our proposed framework for sequence-to-sequence speech recognition and text-to-speech systems . The results reveal that our proposed extension can improve performance significantly compared to a standard attention baseline .
This paper is concerned with isolated speech sequence recognition problem . The dynamic time-warping method is applied for implementation of the very fast real-time speech sequence recognition . An advanced version of the basic algorithm is incorporated in the AREX v1.0 application for the real-time isolated speech sequence recognition .
The systems considered in this paper are based on connectionist models , or artificial neural networks , sometimes combined with statistical techniques for recognition of sequences of patterns , stressing the integration of prior knowledge and learning . Different architectures for sequence and speech recognition are reviewed , including recurrent networks as well as hybrid systems involving hidden Markov models .
A low-power and real-time hidden Markov model ( HMM ) accelerator is proposed for gesture user interfaces on wearable smart devices . HMM algorithm is widely used for sequence recognitions such as speech recognition and gesture recognition due to its best-in-class recognition accuracy . However , the HMM algorithm incorporates high computational complexity and requires massive memory bandwidth for sequence matches .
This paper proposes a forward attention method for the sequence-to-sequence acoustic modeling of speech synthesis . This method is motivated by the nature of the monotonic alignment from phone sequences to acoustic sequences .
The underlying genetic architecture is complex , involving a range of molecular mechanisms . For example , rare protein-coding mutations of the FOXP2 transcription factor cause severe problems with sequencing of speech sounds , while common genetic risk variants of small effect size in genes like CNTNAP2 , ATP2C2 and CMIP are associated with typical forms of language impairment . In this article , we describe how investigations of these and other candidate genes , in humans , animals and cellular models , are unravelling the connections between genes and cognition .
For applications such as speech recognition , where we deal with ordered sequences , it is desirable to impose a left-to-right structure on the model to improve its ability to model the sequential nature of the speech signal . In this paper , we introduce three enhancements to HDP-HMM : ( 1 ) a left-to-right structure : needed for sequential decoding of speech , ( 2 ) non-emitting initial and final states : required for modeling finite length sequences , ( 3 ) HDP mixture emissions : allows sharing of data across states . The latter is particularly important for speech recognition because Gaussian mixture models have been very effective at modeling speaker variability .
For this reason it seems unsuitable as an introduction to neural network techniques for cognitive scientists , psychologists or linguists , although the book might suit those with a strong mathematical or computing background . However , Bengio provides clear descriptions of numerous advanced techniques , the underlying theory and mathematics , and examples of applications in speech and sequence processing . As such , his work may be of considerable'value to those interested in machine learning seeking to apply similar techniques within a psychological framework , or working with raw speech rather than some idealization .
Procedure speech recognition sequences words composed of several words from a given , in which are provided a first recognition method and a second recognition process vocabulary , wherein the first recognition method and the second recognition method are applied to separate segments of a sequence of words to be recognized , wherein the first procedure a recognition procedure bigram with unique syntax integrated as the second recognition process of a recognition procedure applies statistical evaluation of the sequences of words it is applied , and Upon a change of bigram recognition procedure with the second integrated syntax univocal recognition procedure with statistical assessment of word sequences the last two words of the treated segment are grouped according to the first procedure for one nonword that is a bigram recognition method .
Correctly ordering a sequence of speech sounds is a crucial aspect of speech production . Although studies have yielded a rich body of data on the neural substrates of visuomotor sequencing and sequence learning , research on brain regions and their functions involving speech sequence production has n’t attracted much attention until recently . Previous functional MRI studies manipulating the complexity of sequences at the phonemic , syllabic , and suprasyllabic levels have revealed a network of motor-related cortical and sub-cortical speech regions .
Robust Speech Recognition of Uncertain or Missing Data - Theory and Applications Source Separation via Spectral Masking for Speech Recognition Systems Selected methods of pathological speech signal analysis An Image-based Deep Spectrum Feature Representation for the Recognition of Emotional Speech Text-Dependent Speaker Recognition System for Indian Languages Communication system between the outer speech recognition system of vehicle-mounted voice identification system and car and method INTERSPEECH 2006 Discourse Structure and Speech Recognition Problems Speech recognition without a lexicon - bridging the gap between graphemic and phonetic systems Methods of Teaching Speech Recognition Speech Recognition Using Scaly Neural Networks Speech recognition method in a signal processing device and signal processing device Grammar as a Foreign Language Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Light Gated Recurrent Units for Speech Recognition Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions Density estimation using Real NVP Multi-Task Learning as Multi-Objective Optimization The IBM 2016 English Conversational Telephone Speech Recognition System Global-Locally Self-Attentive Dialogue State Tracker Patch-based analysis of visual speech from multiple views Scattering vs. discrete cosine transform features in visual speech processing Audio-visual television broadcast programs processing, transcription, indexing and searching Perceptual Evaluation of Video-Realistic Speech Lip Reading Using Convolutional Auto Encoders as Feature Extractor Dynamic visual features for audio-visual speaker verification Environmentally robust audio-visual speaker identification Lip Segmentation with Muti-scale Features Based on Fully Convolution Network Semi-Supervised Regression using Spectral Techniques A Trust-Based Team Formation Framework for Mobile Intelligence in Smart Factories Creating Simplified Version of Lip Database based on Front View of Face Shape Feature Analysis for Visual Speech and Speaker Recognition Multimodal speech recognition: increasing accuracy using high speed video data Visual words for lip-reading Anti-sequences: event detection by frame stacking Res2Net: A New Multi-scale Backbone Architecture A Novel English Speech Recognition Approach Based on Hidden Markov Model Bilingual Chinese/English voice browsing based on a VoiceXML platform Unsupervised Adaptation With Discriminative Mapping Transforms Adaptive training using discriminative mapping transforms Use of static/dynamic parameters in automatic phonemic segmentation system for english continuous speech Implementation of Flipped Classroom Model and Its Effectiveness on English Speaking Performance Bayesian discriminative adaptation for speech recognition Progress on Mandarin conversational telephone speech recognition A.I. based Embedded Speech to Text Using Deepspeech An alignment matching method to explore pseudosyllable properties across different corpora English Speech Recognition Method Based on Hidden Markov Model Multi-Scale Alignment and Contextual History for Attention Mechanism in Sequence-to-Sequence Model Dynamic time-warping method for isolated speech sequence recognition A Connectionist Approach to Speech Recognition A low-power real-time hidden Markov model accelerator for gesture user interface on wearable devices Forward Attention in Sequence- To-Sequence Acoustic Modeling for Speech Synthesis Decoding the genetics of speech and language A left-to-right HDP-HMM with HDPM emissions Neural Networks for Speech and Sequence Recognition Procedure using a speech recognition grammar. The neuroanatomy of speech sequencing at the syllable level
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ACM Multimedia INTERSPEECH NIPS ICLR ArXiv IEEE Transactions on Emerging Topics in Computational Intelligence 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ICLR NeurIPS INTERSPEECH ACL AVSP AVSP AVSP SLT DSC ICAIC (3) CVPR (2) IEEE transactions on pattern analysis and machine intelligence ICASSP (3) INTERSPEECH ICASSP ISCSLP INTERSPEECH SLT A-SSCC ICASSP CISS
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