Connectionist Models Applied 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 Connectionist Models Applied to Automatic Speech Recognition write by Yoshua Bengio. This book was released on 1987. Connectionist Models Applied to Automatic Speech Recognition available in PDF, EPUB and Kindle.
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."
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.
A Comparative Study of the Traditional Classifier and the Connectionist Model for Speaker Dependent Speech Recognition System
A Comparative Study of the Traditional Classifier and the Connectionist Model for Speaker Dependent Speech Recognition System - 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 A Comparative Study of the Traditional Classifier and the Connectionist Model for Speaker Dependent Speech Recognition System write by Sheikh Hussain Shaikh Salleh. This book was released on 1993. A Comparative Study of the Traditional Classifier and the Connectionist Model for Speaker Dependent Speech Recognition System available in PDF, EPUB and Kindle. 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.
Speech Processing, Recognition and Artificial Neural Networks
Speech Processing, Recognition and Artificial Neural Networks - 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 Speech Processing, Recognition and Artificial Neural Networks write by Gerard Chollet. This book was released on 2012-12-06. Speech Processing, Recognition and Artificial Neural Networks available in PDF, EPUB and Kindle. 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.
