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Automatic Speech Recognition [electronic resource] : A Deep Learning Approach / by Dong Yu, Li Deng.

By: Yu, Dong [author.].
Contributor(s): Deng, Li [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Signals and Communication Technology: Publisher: London : Springer London : Imprint: Springer, 2015Description: XXVI, 321 p. 62 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781447157793.Subject(s): Engineering | Application software | Acoustical engineering | Engineering | Signal, Image and Speech Processing | Engineering Acoustics | Computer Appl. in Social and Behavioral SciencesAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.
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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 provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

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