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Automatic speech recognition = a deep learning approach /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Automatic speech recognition/ by Dong Yu, Li Deng.
Reminder of title:	a deep learning approach /
Author:	Yu, Dong.
other author:	Deng, Li.
Published:	London :Springer London : : 2015.,
Description:	xxvi, 321 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Automatic speech recognition. -
Online resource:	http://dx.doi.org/10.1007/978-1-4471-5779-3
ISBN:	9781447157793 (electronic bk.)

Automatic speech recognition = a deep learning approach /
Yu, Dong.

Automatic speech recognitiona deep learning approach /[electronic resource] :by Dong Yu, Li Deng. - London :Springer London :2015. - xxvi, 321 p. :ill., digital ;24 cm. - Signals and communication technology,1860-4862. - Signals and communication technology..

Section 1: Automatic speech recognition: Background -- Feature extraction: basic frontend -- Acoustic model: Gaussian mixture hidden Markov model -- Language model: stochastic N-gram -- Historical reviews of speech recognition research: 1st, 2nd, 3rd, 3.5th, and 4th generations -- Section 2: Advanced feature extraction and transformation -- Unsupervised feature extraction -- Discriminative feature transformation -- Section 3: Advanced acoustic modeling -- Conditional random field (CRF) and hidden conditional random field (HCRF) -- Deep-Structured CRF -- Semi-Markov conditional random field -- Deep stacking models -- Deep neural network hidden Markov hybrid model -- Section 4: Advanced language modeling -- Discriminative Language model -- Log-linear language model -- Neural network language model.

This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition.

ISBN: 9781447157793 (electronic bk.)

Standard No.: 10.1007/978-1-4471-5779-3doiSubjects--Topical Terms:

557847
Automatic speech recognition.

LC Class. No.: TK7895.S65

Dewey Class. No.: 006.454

Automatic speech recognition = a deep learning approach /
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505 0 $a Section 1: Automatic speech recognition: Background -- Feature extraction: basic frontend -- Acoustic model: Gaussian mixture hidden Markov model -- Language model: stochastic N-gram -- Historical reviews of speech recognition research: 1st, 2nd, 3rd, 3.5th, and 4th generations -- Section 2: Advanced feature extraction and transformation -- Unsupervised feature extraction -- Discriminative feature transformation -- Section 3: Advanced acoustic modeling -- Conditional random field (CRF) and hidden conditional random field (HCRF) -- Deep-Structured CRF -- Semi-Markov conditional random field -- Deep stacking models -- Deep neural network hidden Markov hybrid model -- Section 4: Advanced language modeling -- Discriminative Language model -- Log-linear language model -- Neural network language model.
520 $a This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition.
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