Recurrent Neural Network based Probabilistic Language Model

Speech Recognition with Probabilistic Language Model

Sathyanarayanan Kuppusami (Autor)

Buch | Softcover

60 Seiten

2017
AV Akademikerverlag
9786202205443 (ISBN)

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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.

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.

Erscheinungsdatum	23.09.2022
Sprache	englisch
Maße	150 x 220 mm
Gewicht	106 g
Themenwelt	Mathematik / Informatik ► Informatik ► Netzwerke
Themenwelt	Mathematik / Informatik ► Informatik ► Web / Internet
Schlagworte	Language Model • probabilistic neural model • Recurrent Neural Network
ISBN-13	9786202205443 / 9786202205443
Zustand	Neuware