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Neural Networks for Speech and Sequence Recognition by Yoshua Bengio

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Published by Itp New Media .
Written in English

Subjects:

  • Neural Networks,
  • Pattern Recognition,
  • Artificial Intelligence - General,
  • Networking - General,
  • Neural Computing,
  • Computers - Communications / Networking,
  • Computer Books: General

Book details:

The Physical Object
FormatHardcover
Number of Pages167
ID Numbers
Open LibraryOL11919553M
ISBN 101850321701
ISBN 109781850321705

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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 .   Sequence recognition is a crucial element in many applications in the fields of speech analysis, control and modelling. This text applies the techniques of neural networks and hidden Markov models to the problems of sequence recognition, and as such is intended to prove valuable to researchers and graduate students : Yoshua Bengio. Sequence-to-sequence networks have also achieved many breakthroughs in speech recognition [20, 21, 22]. They can be divided into three modules: an encoding module that transforms sequences, attention module that estimates the alignment between the hidden vector and targets, and decoding module that generates the output by: 1. This book is one of the most up-to-date and cutting-edge texts available on the rapidly growing application area of neural networks. Neural Networks and Pattern Recognition focuses on the use of neural networksin pattern recognition, a very important application area for neural networks technology.

This is the preliminary web site on the upcoming Book on Recurrent Neural Networks, to be published by Cambridge University Press. The authors are: adaptive robotics and control, connected handwriting recognition, image classification, speech recognition, protein analysis, stock market prediction, and other sequence learning problems. Standard Recurrent Neural Networks. Recall that a recurrent neural network is one in which each layer represents another step in time (or another step in some sequence), and that each time step gets one input and predicts one output. However, the network is constrained to use the same "transition function" for each time step, thus learning to predict the output sequence from the input sequence. Title:Speech Recognition with Deep Recurrent Neural Networks. Abstract: Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is by: IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 22, NO. 10, OCTOBER Convolutional Neural Networks for Speech Recognition Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, Li Deng, Gerald Penn, and Dong Yu Abstract—Recently, the hybrid deep neural network (DNN)-Cited by:

Artificial Neural Networks (ANNs) have been widely proposed as a potentially powerful approach to solve difficult problems such as vision, pattern and speech recognition [Hennebert et al., ; Hertz et al., ; Lippmann, ]. a) Present study of artificial neural networks for speech recognition task. Neural network size influence on the effectiveness of detection of phonemes in words. The research methods of speech signal parameterization. Learn about how to use linear prediction analysis, a temporary way of learning of the neural network for recognition of Size: KB. 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. Index Terms— recurrent neural networks, deep neural networks, speech recognition 1. INTRODUCTION Neural networks have a long history in speech recognition, usually in combination with hidden Markov models [1, 2]. They have gained attention in recent years with the dramatic improvements in acoustic modelling yielded by deep feed-forward.