Incorporating Knowledge Sources into Statistical Speech Recognition

Sakriani Sakti

ISBN 10: 1441946764 / ISBN 13: 9781441946768

Published by Springer US, Springer US, 2010

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Incorporating Knowledge Sources into Statistical Speech Recognition addresses the problem of developing efficient automatic speech recognition (ASR) systems, which maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training / recognition effort feasible and improving speech recognition performance. The book provides an efficient general framework to incorporate additional knowledge sources into state-of-the-art statistical ASR systems. It can be applied to many existing ASR problems with their respective model-based likelihood functions in flexible ways.

Seller Inventory # 9781441946768

Bibliographic Details

• Title: Incorporating Knowledge Sources into Statistical Speech Recognition
• Publisher: Springer US, Springer US
• Publication Date: 2010
• Language: English
• ISBN 10: 1441946764
• ISBN 13: 9781441946768
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 13D millimeters 235 millimeters width by 155 millimeters height by 13 millimeters depth
• Weight: 341 grams

About this title

Synopsis

Incorporating Knowledge Sources into Statistical Speech Recognition addresses the problem of developing efficient automatic speech recognition (ASR) systems, which maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training / recognition effort feasible and improving speech recognition performance. The book provides an efficient general framework to incorporate additional knowledge sources into state-of-the-art statistical ASR systems. It can be applied to many existing ASR problems with their respective model-based likelihood functions in flexible ways.

From the Back Cover

Incorporating Knowledge Sources into Statistical Speech Recognition offers solutions for enhancing the robustness of a statistical automatic speech recognition (ASR) system by incorporating various additional knowledge sources while keeping the training and recognition effort feasible.

The authors provide an efficient general framework for incorporating knowledge sources into state-of-the-art statistical ASR systems. This framework, which is called GFIKS (graphical framework to incorporate additional knowledge sources), was designed by utilizing the concept of the Bayesian network (BN) framework. This framework allows probabilistic relationships among different information sources to be learned, various kinds of knowledge sources to be incorporated, and a probabilistic function of the model to be formulated.

Incorporating Knowledge Sources into Statistical Speech Recognition demonstrates how the statistical speech recognition system may incorporate additional information sources by utilizing GFIKS at different levels of ASR. The incorporation of various knowledge sources, including background noises, accent, gender and wide phonetic knowledge information, in modeling is discussed theoretically and analyzed experimentally.