Data Selection and Model Combination in Connectionist Speech Recognition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Selection and Model Combination in Connectionist Speech Recognition write by G. D. Cook. This book was released on 1997. Data Selection and Model Combination in Connectionist Speech Recognition available in PDF, EPUB and Kindle.
Connectionist Speech Recognition
Connectionist Speech Recognition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Connectionist Speech Recognition write by Hervé A. Bourlard. This book was released on 2012-12-06. Connectionist Speech Recognition available in PDF, EPUB and Kindle. 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.
Automatic Speech and Speaker Recognition
Automatic Speech and Speaker Recognition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Automatic Speech and Speaker Recognition write by Chin-Hui Lee. This book was released on 2012-12-06. Automatic Speech and Speaker Recognition available in PDF, EPUB and Kindle. 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.
Some Connectionist Models and Their Application to Automatic Speech Recognition
Some Connectionist Models and Their Application to Automatic Speech Recognition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Some Connectionist Models and Their Application to Automatic Speech Recognition write by Yoshua Bengio. This book was released on 1990. Some Connectionist Models and Their Application to Automatic Speech Recognition available in PDF, EPUB and Kindle. 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."
Intelligent Speech Signal Processing
Intelligent Speech Signal Processing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Intelligent Speech Signal Processing write by Nilanjan Dey. This book was released on 2019-06-15. Intelligent Speech Signal Processing available in PDF, EPUB and Kindle. 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
