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Hierarchical Neural Network Structures for Phoneme Recognition (Signals and Communication Technology) - Tapa blanda
Sinopsis
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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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.
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Hierarchical Neural Network Structures for Phoneme Recognition
Publicado por
Springer Berlin Heidelberg, 2014
ISBN 10: 3642432107
ISBN 13: 9783642432101
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Tapa blanda
Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Simplifies the analysis in spoken language dialogue systemsInvestigates hierarchical structures based on neural networks for automatic speech recognitionWritten for academic and industrial researchers in speech recognitionIn this bo. Nº de ref. del artículo: 5060644
Cantidad disponible: Más de 20 disponibles
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Hierarchical Neural Network Structures for Phoneme Recognition
Publicado por
Springer Berlin Heidelberg Nov 2014, 2014
ISBN 10: 3642432107
ISBN 13: 9783642432101
Nuevo
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 152 pp. Englisch. Nº de ref. del artículo: 9783642432101
Cantidad disponible: 2 disponibles
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Hierarchical Neural Network Structures for Phoneme Recognition
Publicado por
Springer Berlin Heidelberg, 2014
ISBN 10: 3642432107
ISBN 13: 9783642432101
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783642432101
Cantidad disponible: 1 disponibles
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Hierarchical Neural Network Structures for Phoneme Recognition (Signals and Communication Technology)
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Hierarchical Neural Network Structures for Phoneme Recognition
Librería: Chiron Media, Wallingford, Reino Unido
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PF. Condición: New. Nº de ref. del artículo: 6666-IUK-9783642432101
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Hierarchical Neural Network Structures for Phoneme Recognition
Publicado por
Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2014, 2014
ISBN 10: 3642432107
ISBN 13: 9783642432101
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 152 pp. Englisch. Nº de ref. del artículo: 9783642432101
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Hierarchical Neural Network Structures for Phoneme.
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Hierarchical Neural Network Structures for Phoneme.
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Hierarchical Neural Network Structures for Phoneme Recognition
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Paperback. Condición: Brand New. 2013 edition. 152 pages. 9.25x6.10x0.35 inches. In Stock. Nº de ref. del artículo: x-3642432107
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Hierarchical Neural Network Structures for Phoneme.
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Condición: New. PRINT ON DEMAND. Nº de ref. del artículo: 18133607969
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