Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 60 pp. Englisch. N� de ref. del art�culo: 9786202205443

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Recurrent Neural Network based Probabilistic Language Model

Kuppusami, Sathyanarayanan

ISBN 10: 620220544X ISBN 13: 9786202205443

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Recurrent Neural Network based Probabilistic Language Model

Sathyanarayanan Kuppusami

Publicado por AV Akademikerverlag, 2017

ISBN 10: 620220544X ISBN 13: 9786202205443

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Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kuppusami SathyanarayananIn my beloved interest of research in robotics, I obtained my Master s degree in Intelligent Adaptive Systems from Universitaet Hamburg. I have good experience in Machine learning, Neural networks, Ros program. N� de ref. del art�culo: 385934435

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Recurrent Neural Network based Probabilistic Language Model

Sathyanarayanan Kuppusami

Publicado por AV Akademikerverlag Okt 2017, 2017

ISBN 10: 620220544X ISBN 13: 9786202205443

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Taschenbuch. Condici�n: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 60 pp. Englisch. N� de ref. del art�culo: 9786202205443

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

Sathyanarayanan Kuppusami

Publicado por AV Akademikerverlag

ISBN 10: 620220544X ISBN 13: 9786202205443

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Taschenbuch. Condici�n: Neu. 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. N� de ref. del art�culo: 9786202205443

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