Recurrent Neural Network based Probabilistic Language Model: Speech Recognition with Probabilistic Language Model - Softcover
Inhaltsangabe
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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Über die Autorin bzw. den Autor
In my beloved interest of research in robotics, I obtained my Master's degree in Intelligent Adaptive Systems from Universität Hamburg. I have good experience in Machine learning, Neural networks, Ros programming for NAO robot and image processing. I like to explore in the field of decision making from knowledge processing.
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Recurrent Neural Network based Probabilistic Language Model
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Recurrent Neural Network based Probabilistic Language Model
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Recurrent Neural Network based Probabilistic Language Model
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AV Akademikerverlag Okt 2017, 2017
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786202205443
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Recurrent Neural Network based Probabilistic Language Model
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Zustand: 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. Bestandsnummer des Verkäufers 385934435
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Recurrent Neural Network based Probabilistic Language Model
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AV Akademikerverlag Okt 2017, 2017
ISBN 10: 620220544X
ISBN 13: 9786202205443
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Taschenbuch. Zustand: 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 Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Bestandsnummer des Verkäufers 9786202205443
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Recurrent Neural Network based Probabilistic Language Model : Speech Recognition with Probabilistic Language Model
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786202205443
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Recurrent Neural Network based Probabilistic Language Model | Speech Recognition with Probabilistic Language Model
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Taschenbuch. Zustand: Neu. Recurrent Neural Network based Probabilistic Language Model | Speech Recognition with Probabilistic Language Model | Sathyanarayanan Kuppusami | Taschenbuch | 60 S. | Englisch | 2017 | AV Akademikerverlag | EAN 9786202205443 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 110148677
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