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."

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 Dynamic Speech Models written by Li Deng and published by Morgan & Claypool Publishers. This book was released on 2006-12-01 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech dynamics refer to the temporal characteristics in all stages of the human speech communication process. This speech “chain” starts with the formation of a linguistic message in a speaker's brain and ends with the arrival of the message in a listener's brain. Given the intricacy of the dynamic speech process and its fundamental importance in human communication, this monograph is intended to provide a comprehensive material on mathematical models of speech dynamics and to address the following issues: How do we make sense of the complex speech process in terms of its functional role of speech communication? How do we quantify the special role of speech timing? How do the dynamics relate to the variability of speech that has often been said to seriously hamper automatic speech recognition? How do we put the dynamic process of speech into a quantitative form to enable detailed analyses? And finally, how can we incorporate the knowledge of speech dynamics into computerized speech analysis and recognition algorithms? The answers to all these questions require building and applying computational models for the dynamic speech process. What are the compelling reasons for carrying out dynamic speech modeling? We provide the answer in two related aspects. First, scientific inquiry into the human speech code has been relentlessly pursued for several decades. As an essential carrier of human intelligence and knowledge, speech is the most natural form of human communication. Embedded in the speech code are linguistic (as well as para-linguistic) messages, which are conveyed through four levels of the speech chain. Underlying the robust encoding and transmission of the linguistic messages are the speech dynamics at all the four levels. Mathematical modeling of speech dynamics provides an effective tool in the scientific methods of studying the speech chain. Such scientific studies help understand why humans speak as they do and how humans exploit redundancy and variability by way of multitiered dynamic processes to enhance the efficiency and effectiveness of human speech communication. Second, advancement of human language technology, especially that in automatic recognition of natural-style human speech is also expected to benefit from comprehensive computational modeling of speech dynamics. The limitations of current speech recognition technology are serious and are well known. A commonly acknowledged and frequently discussed weakness of the statistical model underlying current speech recognition technology is the lack of adequate dynamic modeling schemes to provide correlation structure across the temporal speech observation sequence. Unfortunately, due to a variety of reasons, the majority of current research activities in this area favor only incremental modifications and improvements to the existing HMM-based state-of-the-art. For example, while the dynamic and correlation modeling is known to be an important topic, most of the systems nevertheless employ only an ultra-weak form of speech dynamics; e.g., differential or delta parameters. Strong-form dynamic speech modeling, which is the focus of this monograph, may serve as an ultimate solution to this problem. After the introduction chapter, the main body of this monograph consists of four chapters. They cover various aspects of theory, algorithms, and applications of dynamic speech models, and provide a comprehensive survey of the research work in this area spanning over past 20~years. This monograph is intended as advanced materials of speech and signal processing for graudate-level teaching, for professionals and engineering practioners, as well as for seasoned researchers and engineers specialized in speech processing

Download or read book Advances in Neural Information Processing Systems 8 written by David S. Touretzky and published by MIT Press. This book was released on 1996 with total page 1128 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book

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 REMAP written by Yochai Konig and published by . This book was released on 1996 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Syllable Articulatory feature and Stress accent Model of Speech Recognition written by Shuangyu Chang and published by . This book was released on 2002 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Readings in Speech Recognition written by Alexander Waibel and published by Elsevier. This book was released on 1990-12-25 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: After more than two decades of research activity, speech recognition has begun to live up to its promise as a practical technology and interest in the field is growing dramatically. Readings in Speech Recognition provides a collection of seminal papers that have influenced or redirected the field and that illustrate the central insights that have emerged over the years. The editors provide an introduction to the field, its concerns and research problems. Subsequent chapters are devoted to the main schools of thought and design philosophies that have motivated different approaches to speech recognition system design. Each chapter includes an introduction to the papers that highlights the major insights or needs that have motivated an approach to a problem and describes the commonalities and differences of that approach to others in the book.

Download or read book Time Map Phonology written by J. Carson-Berndsen and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a revised version of my doctoral thesis which was submitted in April 1993. The main extension is a chapter on evaluation of the system de scribed in Chapter 8 as this is clearly an issue which was not treated in the original version. This required the collection of data, the development of a concept for diagnostic evaluation of linguistic word recognition systems and, of course, the actual evaluation of the system itself. The revisions made primarily concern the presentation of the latest version of the SILPA system described in an additional Subsection 8. 3, the development environment for SILPA in Sec tion 8. 4, the diagnostic evaluation of the system as an additional Chapter 9. Some updates are included in the discussion of phonology and computation in Chapter 2 and finite state techniques in computational phonology in Chapter 3. The thesis was designed primarily as a contribution to the area of compu tational phonology. However, it addresses issues which are relevant within the disciplines of general linguistics, computational linguistics and, in particular, speech technology, in providing a detailed declarative, computationally inter preted linguistic model for application in spoken language processing. Time Map Phonology is a novel, constraint-based approach based on a two-stage temporal interpretation of phonological categories as events.

Download or read book Data Selection and Model Combination in Connectionist Speech Recognition written by G. D. Cook and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Segment based Stochastic Models of Spectral Dynamics for Continuous Speech Recognition written by Vassilios V. Digalakis and published by . This book was released on 1992 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hierarchical Connectionist Acoustic Modeling for Domain Adaptive Large Vocabulary Speech Recognition written by Jürgen Fritsch and published by . This book was released on 2000 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Oxford Handbook of Psycholinguistics written by M. Gareth Gaskell and published by Oxford University Press, USA. This book was released on 2007 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability to communicate through spoken and written language is one of the defining characteristics of the human race, yet it remains a deeply mysterious process. The young science of psycholinguistics attempts to uncover the mechanisms and representations underlying human language. This interdisciplinary field has seen massive developments over the past decade, with a broad expansion of the research base, and the incorporation of new experimental techniques such as brain imaging and computational modelling. The result is that real progress is being made in the understanding of the key components of language in the mind. The Oxford Handbook of Psycholinguistics brings together the views of 75 leading researchers in psycholinguistics to provide a comprehensive and authoritative review of the current state of the art in psycholinguistics. With almost 50 chapters written by experts in the field, the range and depth of coverage is unequalled. The contributors are eminent in a wide range of fields, including psychology, linguistics, human memory, cognitive neuroscience, bilingualism, genetics, development and neuropsychology. Their contributions are organised into six themed sections, covering word recognition, the mental lexicon, comprehension and discourse, language production, language development, and perspectives on psycholinguistics. The breadth of coverage, coupled with the accessibility of the short chapter format should make the handbook essential reading for both students and researchers in the fields of psychology, linguistics and neuroscience.

Download or read book Mathematical Models for Speech Technology written by Stephen Levinson and published by John Wiley & Sons. This book was released on 2005-05-13 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Models of Spoken Language presents the motivations for, intuitions behind, and basic mathematical models of natural spoken language communication. A comprehensive overview is given of all aspects of the problem from the physics of speech production through the hierarchy of linguistic structure and ending with some observations on language and mind. The author comprehensively explores the argument that these modern technologies are actually the most extensive compilations of linguistic knowledge available.Throughout the book, the emphasis is on placing all the material in a mathematically coherent and computationally tractable framework that captures linguistic structure. It presents material that appears nowhere else and gives a unification of formalisms and perspectives used by linguists and engineers. Its unique features include a coherent nomenclature that emphasizes the deep connections amongst the diverse mathematical models and explores the methods by means of which they capture linguistic structure. This contrasts with some of the superficial similarities described in the existing literature; the historical background and origins of the theories and models; the connections to related disciplines, e.g. artificial intelligence, automata theory and information theory; an elucidation of the current debates and their intellectual origins; many important little-known results and some original proofs of fundamental results, e.g. a geometric interpretation of parameter estimation techniques for stochastic models and finally the author's own unique perspectives on the future of this discipline. There is a vast literature on Speech Recognition and Synthesis however, this book is unlike any other in the field. Although it appears to be a rapidly advancing field, the fundamentals have not changed in decades. Most of the results are presented in journals from which it is difficult to integrate and evaluate all of these recent ideas. Some of the fundamentals have been collected into textbooks, which give detailed descriptions of the techniques but no motivation or perspective. The linguistic texts are mostly descriptive and pictorial, lacking the mathematical and computational aspects. This book strikes a useful balance by covering a wide range of ideas in a common framework. It provides all the basic algorithms and computational techniques and an analysis and perspective, which allows one to intelligently read the latest literature and understand state-of-the-art techniques as they evolve.

Download or read book Evolving Connectionist Systems written by Nikola Kasabov and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.

Download or read book Linear Dynamic Models for Automatic Speech Recognition written by Joe Frankel and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The majority of automatic speech recognition (ASR) systems rely on hidden Markov models (HMM), in which the output distribution associated with each state is modelled by a mixture of diagonal covariance Gaussians. Dynamic information is typically included by appending time-derivatives to feature vectors. This approach, whilst successful, makes the false assumption of framewise independence of the augmented feature vectors and ignores the spatial correlations in the parametrised speech signal. This dissertation seeks to address these shortcomings by exploring acoustic modelling for ASR with an application of a form of state-space model, the linear dynamic model (LDM). Rather than modelling individual frames of data, LDMs characterize entire segments of speech. An auto-regressive state evolution through a continuous space gives a Markovian model of the underlying dynamics, and spatial correlations between feature dimensions are absorbed into the structure of the observation process. LDMs have been applied to speech recognition before, however a smoothed Gauss-Markov form was used which ignored the potential for subspace modelling. The continuous dynamical state means that information is passed along the length of each segment. Furthermore, if the state is allowed to be continuous across segment boundaries, long range dependencies are built into the system and the assumption of independence of successive segments is loosened. The state provides an explicit model of temporal correlation which sets this approach apart from frame-based and some segment-based models where the ordering of the data is unimportant. The benefits of such a model are examined both within and between segments. LDMs are well suited to modelling smoothly varying, continuous, yet noisy trajectories such as found in measured articulatory data. Using speaker-dependent data from the MOCHA corpus, the performance of systems which model acoustic, articulatory, and combined acoustic-articulatory features are compared. As well as measured articulatory parameters, experiments use the output of neural networks trained to perform an articulatory inversion mapping. The speaker-independent TIMIT corpus provides the basis for larger scale acoustic-only experiments. Classification tasks provide an ideal means to compare modelling choices without the confounding influence of recognition search errors, and are used to explore issues such as choice of state dimension, front-end acoustic parametrization and parameter initialization. Recognition for segment models is typically more computationally expensive than for frame-based models. Unlike frame-level models, it is not always possible to share likelihood calculations for observation sequences which occur within hypothesized segments that have different start and end times. Furthermore, the Viterbi criterion is not necessarily applicable at the frame level. This work introduces a novel approach to decoding for segment models in the form of a stack decoder with A* search. Such a scheme allows flexibility in the choice of acoustic and language models since the Viterbi criterion is not integral to the search, and hypothesis generation is independent of the particular language model. Furthermore, the time-asynchronous ordering of the search means that only likely paths are extended, and so a minimum number of models are evaluated. The decoder is used to give full recognition results for feature-sets derived from the MOCHA and TIMIT corpora. Conventional train/test divisions and choice of language model are used so that results can be directly compared to those in other studies. The decoder is also used to implement Viterbi training, in which model parameters are alternately updated and then used to re-align the training data.