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Adaptive Integration Of Audio And Visual Information Using Discrete And Semi Continuous Hidden Markov Models In Audiovisual Automatic Speech Recognition
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Book Synopsis Adaptive Integration of Audio and Visual Information Using Discrete and Semi-continuous Hidden Markov Models in Audiovisual Automatic Speech Recognition by : Qin Su
Download or read book Adaptive Integration of Audio and Visual Information Using Discrete and Semi-continuous Hidden Markov Models in Audiovisual Automatic Speech Recognition written by Qin Su and published by . This book was released on 1996 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Application of Hidden Markov Models in Speech Recognition by : Mark Gales
Download or read book The Application of Hidden Markov Models in Speech Recognition written by Mark Gales and published by Now Publishers Inc. This book was released on 2008 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Application of Hidden Markov Models in Speech Recognition presents the core architecture of a HMM-based LVCSR system and proceeds to describe the various refinements which are needed to achieve state-of-the-art performance.
Book Synopsis Hidden Markov Models for Speech Recognition by : X. D. Huang
Download or read book Hidden Markov Models for Speech Recognition written by X. D. Huang and published by . This book was released on 1990-01-01 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Speechreading by Humans and Machines by : David G. Stork
Download or read book Speechreading by Humans and Machines written by David G. Stork and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 681 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is one outcome of the NATO Advanced Studies Institute (ASI) Workshop, "Speechreading by Man and Machine," held at the Chateau de Bonas, Castera-Verduzan (near Auch, France) from August 28 to Septem ber 8, 1995 - the first interdisciplinary meeting devoted the subject of speechreading ("lipreading"). The forty-five attendees from twelve countries covered the gamut of speechreading research, from brain scans of humans processing bi-modal stimuli, to psychophysical experiments and illusions, to statistics of comprehension by the normal and deaf communities, to models of human perception, to computer vision and learning algorithms and hardware for automated speechreading machines. The first week focussed on speechreading by humans, the second week by machines, a general organization that is preserved in this volume. After the in evitable difficulties in clarifying language and terminology across disciplines as diverse as human neurophysiology, audiology, psychology, electrical en gineering, mathematics, and computer science, the participants engaged in lively discussion and debate. We think it is fair to say that there was an atmosphere of excitement and optimism for a field that is both fascinating and potentially lucrative. Of the many general results that can be taken from the workshop, two of the key ones are these: • The ways in which humans employ visual image for speech recogni tion are manifold and complex, and depend upon the talker-perceiver pair, severity and age of onset of any hearing loss, whether the topic of conversation is known or unknown, the level of noise, and so forth.
Book Synopsis Multiple Codebook Semi-continuous Hidden Markov Models for Speaker-independent Continuous Speech Recognition by : X. D. Huang
Download or read book Multiple Codebook Semi-continuous Hidden Markov Models for Speaker-independent Continuous Speech Recognition written by X. D. Huang and published by . This book was released on 1989 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "A semi-continuous hidden Markov model based on multiple vector quantization codebooks is used here for large-vocabulary speaker-independent continuous speech recognition. In the techniques employed here, the semi-continuous output probability density function for each codebook is represented by a combination of the corresponding discrete output probabilities of the hidden Markov model and the continuous Gaussian density functions of each individual codebook. Parameters of the vector quantization codebook and the hidden Markov model are mutually optimized to achieve an optimal model/codebook combination under a unified probabilistic framework. Another advantage of this approach is the enhanced robustness of the semi-continuous output probability density function by the combination of multiple codewords and multiple codebooks. For a 1000-word speaker-independent continuous speech recognition using a word-pair grammar, the recognition error rate of the semi-continuous hidden Markov model was reduced by more than 29% and 40% in comparison to the discrete and continuous mixture hidden Markov model respectively."
Book Synopsis Online Learning of Large Margin Hidden Markov Models for Automatic Speech Recognition by : Chih-Chieh Cheng
Download or read book Online Learning of Large Margin Hidden Markov Models for Automatic Speech Recognition written by Chih-Chieh Cheng and published by . This book was released on 2011 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last two decades, large margin methods have yielded excellent performance on many tasks. The theoretical properties of large margin methods have been intensively studied and are especially well-established for support vector machines (SVMs). However, the scalability of large margin methods remains an issue due to the amount of computation they require. This is especially true for applications involving sequential data. In this thesis we are motivated by the problem of automatic speech recognition (ASR) whose large-scale applications involve training and testing on extremely large data sets. The acoustic models used in ASR are based on continuous-density hidden Markov models (CD-HMMs). Researchers in ASR have focused on discriminative training of HMMs, which leads to models with significantly lower error rates. More recently, building on the successes of SVMs and various extensions thereof in the machine learning community, a number of researchers in ASR have also explored large margin methods for discriminative training of HMMs. This dissertation aims to apply various large margin methods developed in the machine learning community to the challenging large-scale problems that arise in ASR. Specifically, we explore the use of sequential, mistake-driven updates for online learning and acoustic feature adaptation in large margin HMMs. The updates are applied to the parameters of acoustic models after the decoding of individual training utterances. For large margin training, the updates attempt to separate the log-likelihoods of correct and incorrect transcriptions by an amount proportional to their Hamming distance. For acoustic feature adaptation, the updates attempt to improve recognition by linearly transforming the features computed by the front end. We evaluate acoustic models trained in this way on the TIMIT speech database. We find that online updates for large margin training not only converge faster than analogous batch optimizations, but also yield lower phone error rates than approaches that do not attempt to enforce a large margin. We conclude this thesis with a discussion of future research directions, highlighting in particular the challenges of scaling our approach to the most difficult problems in large-vocabulary continuous speech recognition.
Book Synopsis Temporal Asynchronicity Modeling by Product HMMS for Audio-Visual Speech Recognition by : Satoshi Nakamura
Download or read book Temporal Asynchronicity Modeling by Product HMMS for Audio-Visual Speech Recognition written by Satoshi Nakamura and published by . This book was released on 2002 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: There have been higher demands recently for Automatic Speech Recognition (ASR) systems able to operate robustly in acoustically noisy environments. This paper proposes a method to effectively integrate audio and visual information in audio-visual (bi-modal) ASR systems. Such integration inevitably necessitates modeling of the synchronization and asynchronization of the audio and visual information. To address the time lag and correlation problems in individual features between speech and lip movements, we introduce a type of integrated HMM modeling of audio-visual information based on a family of a product HMM. The proposed model can represent state synchronicity not only within a phoneme but also between phonemes. Furthermore, we also propose a rapid stream weight optimization based on GPD algorithm for noisy bi-modal speech recognition. Evaluation experiments show that the proposed method improves the recognition accuracy for noisy speech. in SNR=0db our proposed method attained 16% higher performance compared to a product HMMs without the synchronicity re-estimation.
Book Synopsis Semi-continuous Hidden Markov Models for Speech Recognition by : Xuedong Huang
Download or read book Semi-continuous Hidden Markov Models for Speech Recognition written by Xuedong Huang and published by . This book was released on 1989 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sub-word Modeling in Speech Recognition Using Discrete, Continuous Mixture and Semi Continuous Hidden Markov Models by : Hui Kung Lau
Download or read book Sub-word Modeling in Speech Recognition Using Discrete, Continuous Mixture and Semi Continuous Hidden Markov Models written by Hui Kung Lau and published by . This book was released on 1995 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Towards Adaptive Spoken Dialog Systems by : Alexander Schmitt
Download or read book Towards Adaptive Spoken Dialog Systems written by Alexander Schmitt and published by Springer Science & Business Media. This book was released on 2012-09-19 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Spoken Dialog Systems (SDS). The presented stochastic methods allow a flexible, portable and accurate use. Beginning with the foundations of machine learning and pattern recognition, this monograph examines how frequently users show negative emotions in spoken dialog systems and develop novel approaches to speech-based emotion recognition using hybrid approach to model emotions. The authors make use of statistical methods based on acoustic, linguistic and contextual features to examine the relationship between the interaction flow and the occurrence of emotions using non-acted recordings several thousand real users from commercial and non-commercial SDS. Additionally, the authors present novel statistical methods that spot problems within a dialog based on interaction patterns. The approaches enable future SDS to offer more natural and robust interactions. This work provides insights, lessons and inspiration for future research and development, not only for spoken dialog systems, but for data-driven approaches to human-machine interaction in general.
Book Synopsis Cognitively Inspired Audiovisual Speech Filtering by : Andrew Abel
Download or read book Cognitively Inspired Audiovisual Speech Filtering written by Andrew Abel and published by Springer. This book was released on 2015-08-07 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a summary of the cognitively inspired basis behind multimodal speech enhancement, covering the relationship between audio and visual modalities in speech, as well as recent research into audiovisual speech correlation. A number of audiovisual speech filtering approaches that make use of this relationship are also discussed. A novel multimodal speech enhancement system, making use of both visual and audio information to filter speech, is presented, and this book explores the extension of this system with the use of fuzzy logic to demonstrate an initial implementation of an autonomous, adaptive, and context aware multimodal system. This work also discusses the challenges presented with regard to testing such a system, the limitations with many current audiovisual speech corpora, and discusses a suitable approach towards development of a corpus designed to test this novel, cognitively inspired, speech filtering system.
Book Synopsis Integration of Multi-layer Perception and Hidden Markov Models for Automatic Speech Recognition by : Yosu Arriola
Download or read book Integration of Multi-layer Perception and Hidden Markov Models for Automatic Speech Recognition written by Yosu Arriola and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Automatic Speech Recognition Using the Hidden Markov Model by : Satwant Singh
Download or read book Automatic Speech Recognition Using the Hidden Markov Model written by Satwant Singh and published by . This book was released on 1990 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Audiovisual Speech Processing for Robust Voice Activity Detection and Automatic Speech Recognition by : Fei Tao (Electrical engineer)
Download or read book Advances in Audiovisual Speech Processing for Robust Voice Activity Detection and Automatic Speech Recognition written by Fei Tao (Electrical engineer) and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech processing systems are widely used in existing commercial applications, including virtual assistants in smartphones and home assistant devices. Speech-based commands provide convenient hands-free functionality for users. Two key speech processing systems in practical applications are voice activity detection (VAD), which aims to detect when a user is speaking to a system, and automatic speech recognition (ASR), which aims to recognize what the user is speaking. A limitation in these speech tasks is the drop in performance observed in noisy environments or when the speech mode differs from neutral speech (e.g., whisper speech). Emerging audiovisual solutions provide principled frameworks to increase the robustness of the systems by incorporating features describing lip motion. This study proposes novel audiovisual solutions for VAD and ASR tasks. The dissertation introduces unsupervised and supervised audiovisual voice activity detection (AV-VAD). The unsupervised approach combines visual features that are characteristic of the semi-periodic nature of the articulatory production around the orofacial area. The visual features are combined using principal component analysis (PCA) to obtain a single feature. The threshold between speech and non-speech activity is automatically estimated with the expectation-maximization (EM) algorithm. The decision boundary is improved by using the Bayesian information criterion (BIC) algorithm, resolving temporal ambiguities caused by different sampling rates and anticipatory movements. The supervised framework corresponds to the bimodal recurrent neural network (BRNN), which captures the taskrelated characteristics in the audio and visual inputs, and models the temporal information within and across modalities. The approach relied on three subnetworks implemented with long short-term memory (LSTM) networks. This framework is implemented with either hand-crafted features or features representations directly derived from the data (i.e., end-toend system). The study also extends this framework by increasing the temporal modeling by using advanced LSTMs (A-LSTMs). For audiovisual automatic speech recognition (AV-ASR), the study explores the use of visual features to compensate for the mismatch observed when the system is evaluated with whisper speech. We propose supervised adaptation schemes which significantly reduce the mismatch between normal and whisper speech across speakers. The study also introduces the Gating neural network (GNN). The GNN aims to attenuate the effect of unreliable features, creating AV-ASR systems that improve, or at least maintain, the performance of an ASR system implemented only with speech. Finally, the dissertation introduces the front-end alignment neural network (AliNN) to address the temporal alignment problem between audio and visual features. This front-end system is important as the lip motion often precedes speech (e.g., anticipatory movements). The framework relies on RNN with attention model. The resulting aligned features are concatenated and fed to conventional back-end ASR systems obtaining performance improvements. The proposed approaches for AV-VAD and AV-ASR systems are evaluated on large audiovisual corpora, achieving competitive performance under real world scenarios, outperforming conventional audio-based VAD and ASR systems or alternative audiovisual systems proposed by previous studies. Taken collectively, this dissertation has made algorithmic advancements for audiovisual systems, representing novel contributions to the field of multimodal processing.
Book Synopsis Hidden Markov Models for Automatic Speech Recognition by : Stephen Christopher Austin
Download or read book Hidden Markov Models for Automatic Speech Recognition written by Stephen Christopher Austin and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing by : Tong Zhang
Download or read book Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing written by Tong Zhang and published by Springer Science & Business Media. This book was released on 2001-01-31 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing is an up-to-date overview of audio and video content analysis. Included is extensive treatment of audiovisual data segmentation, indexing and retrieval based on multimodal media content analysis, and content-based management of audio data. In addition to the commonly studied audio types such as speech and music, the authors have included hybrid types of sounds that contain more than one kind of audio component such as speech or environmental sound with music in the background. Emphasis is also placed on semantic-level identification and classification of environmental sounds. The authors introduce a new generic audio retrieval system on top of the audio archiving schemes. Both theoretical analysis and implementation issues are presented. The developing MPEG-7 standards are explored. Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing will be especially useful to researchers and graduate level students designing and developing fully functional audiovisual systems for audio/video content parsing of multimedia streams.
Book Synopsis Speech Recognition in Noisy Environments Using Discrete Hidden Markov Models by : Francisco Javier Hernando Pericás
Download or read book Speech Recognition in Noisy Environments Using Discrete Hidden Markov Models written by Francisco Javier Hernando Pericás and published by . This book was released on 1994 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt:
