Recurrent Neural Network based Probabilistic Language Model : Speech Recognition with Probabilistic Language Model

Language: English

Published by AV Akademikerverlag

620220544X / 9786202205443

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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nach der Bestellung gedruckt Neuware - Printed after ordering - Statistical n-gram language models are widely used for their state of the art performance in a continuous speech recognition system. In a domain based scenario, the sequences vary at large for expressing same context by the speakers. But, holding all possible sequences in training corpora for estimating n-gram probabilities is practically difficult. Capturing long distance dependencies from a sequence is an important feature in language models that can provide non zero probability for a sparse sequence during recognition. A simpler back-off n-gram model has a problem of estimating the probabilities for sparse data, if the size of n gram increases. Also deducing knowledge from training patterns can help the language models to generalize on an unknown sequence or word by its linguistic properties like noun, singular or plural, novel position in a sentence. For a weaker generalization, n-gram model needs huge sizes of corpus for training. A simple recurrent neural network based language model approach is proposed here to efficiently overcome the above difficulties for domain based corpora.

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Bibliographic details

Title
Recurrent Neural Network based Probabilistic Language Model : Speech Recognition with Probabilistic Language Model
Author
Sathyanarayanan Kuppusami
Publisher
AV Akademikerverlag
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
620220544X
ISBN 13
9786202205443
Item weight
107 grams
Dimensions
220x150x5 mm

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

Shipping rates from Germany to U.S.A.

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