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Hierarchical Neural Network Structures for Phoneme Recognition
     

Hierarchical Neural Network Structures for Phoneme Recognition

by Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
 

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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

Overview

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.

Editorial Reviews

From the Publisher
From the reviews:

“This brief book comes packed with useful information about some novel techniques for the recognition of speech building blocks known as phonemes. … it is brimming with useful and well-presented information. I recommend it for graduate students in the field, as well as for practicing professionals.” (Vladimir Botchev, Computing Reviews, May, 2013)

Product Details

ISBN-13:
9783642344244
Publisher:
Springer Berlin Heidelberg
Publication date:
10/18/2012
Series:
Signals and Communication Technology Series
Edition description:
2013
Pages:
134
Product dimensions:
6.10(w) x 9.25(h) x (d)

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