Hierarchical Neural Network Structures for Phoneme Recognition: Signals and Communication Technology
Autor Daniel Vasquez, Rainer Gruhn, Wolfgang Minkeren Limba Engleză Hardback – 18 oct 2012
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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
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
ResearchCuprins
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)
“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