Download or read book Connectionist Models Applied to Automatic Speech Recognition written by Yoshua Bengio and published by . This book was released on 1987 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Some Connectionist Models and Their Application to Automatic Speech Recognition written by Yoshua Bengio and published by . This book was released on 1990 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "We attempt to apply some connectionist models to automatic speech recognition. To do so we first consider ways to take advantage of a-priori knowledge in the design of those models. For example we consider the influence on generalization of various preprocessing methods, of the output coding and supervision as well as the architectural design. Recurrent neural networks contain cycles that enable them to retain some information about their past history in order to better predict the next output given the current input. Hence we describe two learning algorithms for these networks, one for general architectures (but not local in time) and one for constrained architectures with self- loops only. Given the importance of cpu requirements for back-propagation algorithms, we discuss some simple methods that can greatly accelerate the convergence of gradient descent with the back-propagation algorithm. In particular we introduce an original technique that provides a different learning rate to different layers of a multi-layered sigmoid network. We then study an alternative type of networks based on Radial Basis Functions (local representation) that can be initialized very fast. We present in detail the results of several experiments with these networks on the recognition of phonemes for the TIMIT databases (speaker-independent, continuous speech database). We propose an acceleration scheme for Radial Basis Functions based on a fast search of the subset of active hidden units. After considering successful networks that combine gaussian units and sigmoid units in a network we propose a cognitively relevant model that combines both a local representation and and [sic] a distributed representation subnetworks to which correspond respectively a fast-learning and a slow-learning capability. This system is based on a reorganization phase during which the information about prototypes and outliers stored in the local subsystem is transferred to the distributed representation subsystem."

Download or read book Connectionist Speech Recognition written by Hervé A. Bourlard and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state of the art continuous speech recognition systems based on hidden Markov models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e. HMM emission probability estimation and feature extraction. The book describes a successful five-year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical systems. Using standard databases and comparison with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. The book is also suitable as a text for advanced courses on neural networks or speech processing.

Download or read book A Comparative Study of the Traditional Classifier and the Connectionist Model for Speaker Dependent Speech Recognition System written by Sheikh Hussain Shaikh Salleh and published by . This book was released on 1993 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Srudies to develop technique and system which allow computers to accept speech inputs have been actively studied since the fifties. The natural question to ask is why study speech recognition. For practical reason speech recognition will solve problems, improve productivity and most important of all it will change the way we live today. As we improve algorithms and have faster machine, it appears that man-machine interface by voice will be a reality within our lifetime. In short term applciation spepech could be used to aid the handicapped (wheelchairs, robotic aid, control system, etc). A comparative study was made using different algorithms to cahiece the short term goal. the three models to be dexcribed are the LPC/DTW, LPC/DTW?VQ and the Neural Network. The fist two model used the template based approach. Distance measures are used to compare templates to find the best match. Dynamic programming is used to solve temporal difference. The technique of data compression is applied to one of these models. The other approach to speech recognition is the connectionist method. This is the most recent development in speech recognition. Connectionist apparocah consistes of many simple computing elements. Connection between these elements are of varying strength. The connection are trained to recognize speech. Statistical evaluation on a prototype system utilizing the recognition methjods mentioned above is as follows; The first model performs 95% recognition accuracy, the second model 92% accuracy and the connectionist model has 59% accuracy in normal quiet room.

Download or read book Automatic Speech and Speaker Recognition written by Chin-Hui Lee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization. Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.

Download or read book Speaker Perception and Recognition An Integrative Framework for Computational Speech Processing written by Oxana Lapteva and published by kassel university press GmbH. This book was released on 2011 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Speech Recognition and Understanding written by Pietro Laface and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book collects the contributions to the NATO Advanced Study Institute on "Speech Recognition and Understanding: Recent Advances, Trends and Applications", held in Cetraro, Italy, during the first two weeks of July 1990. This Institute focused on three topics that are considered of particular interest and rich of i'p.novation by researchers in the fields of speech recognition and understanding: Advances in Hidden Markov modeling, connectionist approaches to speech and language modeling, and linguistic processing including language and dialogue modeling. The purpose of any ASI is that of encouraging scientific communications between researchers of NATO countries through advanced tutorials and presentations: excellent tutorials were offered by invited speakers that present in this book 15 papers which sum marize or detail the topics covered in their lectures. The lectures were complemented by discussions, panel sections and by the presentation of related works carried on by some of the attending researchers: these presentations have been collected in 42 short contributions to the Proceedings. This volume, that the reader can find useful for an overview, although incomplete, of the state of the art in speech understanding, is divided into 6 Parts.

Download or read book Discriminative Connectionist Approaches for Automatic Speech Recognition in Cars written by Joan Marí Hilario and published by . This book was released on 2004 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Connectionist Models for Intelligent Computation written by H. H. Chen and published by . This book was released on 1988 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: We have continued our study of higher order neural networks. The superior processing power capacity and speed of the higher order neural network has been demonstrated for many tasks including text to speech, character recognitions, noise removal, time series prediction etc. Currently, we are applying it to the speech recognition problem. We have constructed a neural network to learn the task of stereopsis from random dot stereogram. The connection weights of the network are computed analytically from the Hebbion learning rule. The results show that the continuity and uniqueness constraints first proposed by Marr and Poggio are learned automatically. We proposed a novel scheme (PSIN) to automatically build a neural network while learning. The new scheme takes advantage of both the parallel and sequential strategies to solve a pattern classification or decision problem. We optimize an entropy measure to encourage the network to extract the best feature first to classify the pattern. Preliminary test of this new scheme shows that PSIN performs superior than the back propagation scheme in hard problems. (KR).

Download or read book Connectionist Combination of Evidence Sources in Automatic Speech Recognition written by David Charles Abberley and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Intelligent Speech Signal Processing written by Nilanjan Dey and published by Academic Press. This book was released on 2019-06-15 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics related information, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. It provides a forum for readers to discover the characteristics of intelligent speech signal processing systems across different domains. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multi-disciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, implementation, development, and management of intelligent systems, neural networks, and related machine learning techniques for speech signal processing. Highlights different data analytics techniques in speech signal processing, including machine learning, and data mining Illustrates different applications and challenges across the design, implementation, and management of intelligent systems and neural networks techniques for speech signal processing Includes coverage of biomodal speech recognition, voice activity detection, spoken language and speech disorder identification, automatic speech to speech summarization, and convolutional neural networks

Download or read book Nonlinear Speech Modeling and Applications written by Gerard Chollet and published by Springer. This book was released on 2005-07-12 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the revised tutorial lectures given at the International Summer School on Nonlinear Speech Processing-Algorithms and Analysis held in Vietri sul Mare, Salerno, Italy in September 2004. The 14 revised tutorial lectures by leading international researchers are organized in topical sections on dealing with nonlinearities in speech signals, acoustic-to-articulatory modeling of speech phenomena, data driven and speech processing algorithms, and algorithms and models based on speech perception mechanisms. Besides the tutorial lectures, 15 revised reviewed papers are included presenting original research results on task oriented speech applications.

Download or read book Computational Models of Speech Pattern Processing written by Keith Ponting and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Helier, Jersey, UK, July 7-18, 1997

Download or read book Advances In Pattern Recognition Systems Using Neural Network Technologies written by Patrick S P Wang and published by World Scientific. This book was released on 1994-01-01 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contents:A Connectionist Approach to Speech Recognition (Y Bengio)Signature Verification Using a “Siamese” Time Delay Neural Network (J Bromley et al.)Boosting Performance in Neural Networks (H Drucker et al.)An Integrated Architecture for Recognition of Totally Unconstrained Handwritten Numerals (A Gupta et al.)Time-Warping Network: A Neural Approach to Hidden Markov Model Based Speech Recognition (E Levin et al.)Computing Optical Flow with a Recurrent Neural Network (H Li & J Wang)Integrated Segmentation and Recognition through Exhaustive Scans or Learned Saccadic Jumps (G L Martin et al.)Experimental Comparison of the Effect of Order in Recurrent Neural Networks (C B Miller & C L Giles)Adaptive Classification by Neural Net Based Prototype Populations (K Peleg & U Ben-Hanan)A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes: A Pilot Study (L Wiskott & C von der Malsburg)and other papers Readership: Computer scientists and engineers.

Download or read book Proceedings of the 1988 Connectionist Models Summer School written by David S. Touretzky and published by . This book was released on 1989 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: The student papers selected for this volume have been revised to incorporate feedback the students received from their peers during the program, and afterwards from their assigned editor (one of us). A few papers describe work that actually took place during the summer school, or immediately after it. The faculty papers are likewise timely and of very high quality. Readers wishing to monitor the neural network scene will find there is much to learn about the current state of the art.

Download or read book Connectionist Approaches to the Deployment of Prior Knowledge for Improving Robustness in Automatic Speech Recognition written by Shahla Parveen and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Modeling Dynamics in Connectionist Speech Recognition the Time Index Model written by International Computer Science Institute and published by . This book was released on 1994 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Here, we introduce an alternative to the Hidden Markov Model (HMM) as the underlying representation of speech production. HMMs suffer from well known limitations, such as the unrealistic assumption that the observations generated in a given state are independent and identically distributed (i.i.d). We propose a time index model that explicitly conditions the emission probability of a state on the time index, i.e., on the number of 'visits' in the current state of the Markov chain in a sequence. Thus, the proposed model does not require an i.i.d. assumption. The connectionist framework enables us to represent the dependence on the time index as a non-parametric distribution and to share parameters between different speech unit models. Furthermore, we discuss an extension to the basic time index model by incorporating information about the duration of the phone segments. Our initial results show that given the position of the boundaries between basic speech units, e.g., phones, we can improve our current connectionist system performance significantly by using this model. However, we still do not know whether these boundaries can be estimated reliably, nor do we know how much benefit we can obtain from this method given less accurate boundary information. Currently we are experimenting with two possible approaches: trying to learn smooth probability densities for the boundaries, and getting a set of reasonable segmentations from an N-Best search. In both cases we will need to consider the effect of incorrect boundaries, since they will undoubtedly occur."