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Hierarchical Neural Network Structures for Phoneme Recognition de Daniel Vasquez

Hierarchical Neural Network Structures for Phoneme Recognition: Signals and Communication Technology

Autor Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
en Limba Engleză Hardback – 18 oct 2012
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.
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Specificații

ISBN-13: 9783642344244
ISBN-10: 3642344240
Pagini: 152
Ilustrații: XVIII, 134 p.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.36 kg
Ediția:2013
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Signals and Communication Technology

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Background in Speech Recognition.- Phoneme Recognition Task.- Hierarchical Approach and Downsampling Schemes.- Extending the Hierarchical Scheme: Inter and Intra Phonetic Information.- Theoretical framework for phoneme recognition analysis.

Recenzii

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)

Textul de pe ultima copertă

In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

Caracteristici

Simplifies the analysis in spoken language dialogue systems Investigates hierarchical structures based on neural networks for automatic speech recognition Written for academic and industrial researchers in speech recognition