Download or read book Modular Neural Networks for Speech Recognition written by Carnegie-Mellon University. Computer Science Dept and published by . This book was released on 1996 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "In recent years, researchers have established the viability of so called hybrid NN/HMM large vocabulary, speaker independent continuous speech recognition systems, where neural networks (NN) are used for the estimation of acoustic emission probabilities for hidden Markov models (HMM) which provide statistical temporal modeling. Work in this direction is based on a proof, that neural networks can be trained to estimate posterior class probabilities. Advantages of the hybrid approach over traditional mixture of Gaussians based systems include discriminative training, fewer parameters, contextual inputs and faster sentence decoding. However, hybrid systems usually have training times that are orders of magnitude higher that those observed in traditional systems. This is largely due to the costly, gradient-based error-backpropagation learning algorithm applied to very large neural networks, which often requires the use of specialized parallel hardware. This thesis examines how a hybrid NN/HMM system can benefit from the use of modular and hierarchical neural networks such as the hierarchical mixture of experts (HME) architecture. Based on a powerful statistical framework, it is shown that modularity and the principle of divide-and-conquer applied to neural network learning reduces training times significantly. We developed a hybrid speech recognition system based on modular neural networks and the state-of-the- art continuous density HMM speech recognizer JANUS. The system is evaluated on the English Spontaneous Scheduling Task (ESST), a 2400 word spontaneous speech database. We developed an adaptive tree growing algorithm for the hierarchical mixtures of experts, which is shown to yield better usage of the parameters of the architecture than a pre-determined topology. We also explored alternative parameterizations of expert and gating networks based on Gaussian classifiers, which allow even faster training because of near-optimal initialization techniques. Finally, we enhanced our originally context independent hybrid speech recognizer to model polyphonic contexts, adopting decision tree clustered context classes from a Gaussian mixtures system."
Download or read book Modular Neural Networks and Type 2 Fuzzy Systems for Pattern Recognition written by Patricia Melin and published by Springer Science & Business Media. This book was released on 2011-10-18 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.
Download or read book Neural Networks for Speech and Sequence Recognition written by Yoshua Bengio and published by London ; Toronto : International Thomson Computer Press. This book was released on 1996 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sequence recognition is a crucial element in many applications in the fields of speech analysis, control, and modeling. This book applies the techniques of neural networks and hidden Markov models to the problems of sequence recognition, and as such will prove valuable to researchers and graduate students alike.
Download or read book Predictive Modular Neural Networks written by Vassilios Petridis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.
Download or read book Neural Networks and Speech Processing written by David P. Morgan and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: We would like to take this opportunity to thank all of those individ uals who helped us assemble this text, including the people of Lockheed Sanders and Nestor, Inc., whose encouragement and support were greatly appreciated. In addition, we would like to thank the members of the Lab oratory for Engineering Man-Machine Systems (LEMS) and the Center for Neural Science at Brown University for their frequent and helpful discussions on a number of topics discussed in this text. Although we both attended Brown from 1983 to 1985, and had offices in the same building, it is surprising that we did not meet until 1988. We also wish to thank Kluwer Academic Publishers for their profes sionalism and patience, and the reviewers for their constructive criticism. Thanks to John McCarthy for performing the final proof, and to John Adcock, Chip Bachmann, Deborah Farrow, Nathan Intrator, Michael Perrone, Ed Real, Lance Riek and Paul Zemany for their comments and assistance. We would also like to thank Khrisna Nathan, our most unbi ased and critical reviewer, for his suggestions for improving the content and accuracy of this text. A special thanks goes to Steve Hoffman, who was instrumental in helping us perform the experiments described in Chapter 9.
Download or read book Modular Neural Networks and Type 2 Fuzzy Systems for Pattern Recognition written by Patricia Melin and published by Springer. This book was released on 2011-10-25 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.
Download or read book Towards Hybrid and Adaptive Computing written by Anupam Shukla and published by Springer Science & Business Media. This book was released on 2010-08-17 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soft Computing today is a very vast field whose extent is beyond measure. This book offers a well structured presentation of the basic concepts of Artificial Neural Networks, Fuzzy Inference Systems and Evolutionary Algorithms.
Download or read book Combining Artificial Neural Nets written by Amanda J.C. Sharkey and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, written by leading researchers, presents methods of combining neural nets to improve their performance. The techniques include ensemble-based approaches, where a variety of methods are used to create a set of different nets trained on the same task, and modular approaches, where a task is decomposed into simpler problems. The techniques are also accompanied by an evaluation of their relative effectiveness and their application to a variety of problems.
Download or read book Applications of Neural Network Models in Automatic Speech Recognition written by Andrew S. Noetzel and published by . This book was released on 1986 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organizations of computing elements that follow the principles of physiological neurons, called neural network models, have been shown to have the capability of learning to recognize patterns and to retrieve complete patterns from partial representations. The implementation of neural network models as VLSI or USLI chips within a few years is certain. This report reviews a number of published papers on neural network models and their capabilities. Then, an outline of a speech recognition system that uses neural network modules for learning and recognition is proposed. It is based on the layered structure of existing speech recognition systems, and uses forced learning (feedback) for conditioning the neural modules at the various levels. (Author).
Download or read book Common LISP Modules written by Mark Watson and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: While creativity plays an important role in the advancement of computer science, great ideas are built on a foundation of practical experience and knowledge. This book presents programming techniques which will be useful in both AI projects and more conventional software engineering endeavors. My primary goal is to enter tain, to introduce new technologies and to provide reusable software modules for the computer programmer who enjoys using programs as models for solutions to hard and interesting problems. If this book succeeds in entertaining, then it will certainly also educate. I selected the example application areas covered here for their difficulty and have provided both program examples for specific applications and (I hope) the method ology and spirit required to master problems for which there is no obvious solution. I developed the example programs on a Macintosh TM using the Macintosh Common LISP TM development system capturing screen images while the example programs were executing. To ensure portability to all Common LISP environments, I have provided a portable graphics library in Chapter 2. All programs in this book are copyrighted by Mark Watson. They can be freely used in any free or commercial software systems if the following notice appears in the fine print of the program's documentation: "This program contains software written by Mark Watson." No royalties are required. The program miniatures contained in this book may not be distributed by posting in source code form on public information networks, or in printed form without my written permission.
Download or read book Speech Processing Recognition and Artificial Neural Networks written by Gerard Chollet and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech Processing, Recognition and Artificial Neural Networks contains papers from leading researchers and selected students, discussing the experiments, theories and perspectives of acoustic phonetics as well as the latest techniques in the field of spe ech science and technology. Topics covered in this book include; Fundamentals of Speech Analysis and Perceptron; Speech Processing; Stochastic Models for Speech; Auditory and Neural Network Models for Speech; Task-Oriented Applications of Automatic Speech Recognition and Synthesis.
Download or read book Handbook of Neural Networks for Speech Processing written by Shigeru Katagiri and published by Artech House Publishers. This book was released on 2000 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here are the comprehensive details on cutting edge technologies employing neural networks for speech recognition and speech processing in modern communications. Going far beyond the simple speech recognition technologies on the market today, this new book, written by and for speech and signal processing engineers in industry, R&D, and academia, takes you to the forefront of the hottest emergent neural net-based speech processing techniques.
Download or read book New Era for Robust Speech Recognition written by Shinji Watanabe and published by Springer. This book was released on 2017-10-30 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
Download or read book Handbook of Neural Network Signal Processing written by Yu Hen Hu and published by CRC Press. This book was released on 2018-10-03 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view. The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.
Download or read book Automatic Speech Recognition written by Dong Yu and published by Springer. This book was released on 2014-11-11 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.
Download or read book Neural Networks written by Richard Kendall Miller and published by . This book was released on 1990 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Hierarchical Neural Network Structures for Phoneme Recognition written by Daniel Vasquez and published by Springer Science & Business Media. This book was released on 2012-10-18 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are mainly evaluated within the phoneme recognition task under the Hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) paradigm. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron (MLP). Additionally, the output of the first level is used as an input for the second level. This system can be substantially speeded up by removing the redundant information contained at the output of the first level.
