Incorporating Knowledge Sources into Statistical Speech Recognition

Nakamura, Satoshi; Sakti, Sakriani; Markov, Konstantin; Minker, Wolfgang

ISBN 10: 1441946764 / ISBN 13: 9781441946768

Editore: Springer-Verlag New York Inc., 2010

The authors address the problem of developing efficient automatic speech recognition systems that maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training manageable and improving speech recognition performance. Series: Lecture Notes in Electrical Engineering. Num Pages: 220 pages, 100 black & white illustrations, 15 black & white tables, biography. BIC Classification: PHDS; THR; TJK; TTBM; UKN. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 11. Weight in Grams: 343. . 2010. 1st ed. Softcover of orig. ed. 2009. Paperback. . . . . Books ship from the US and Ireland. Codice articolo V9781441946768

Riassunto:

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.

Dalla quarta di copertina:

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.

Dati bibliografici

Titolo: Incorporating Knowledge Sources into ...
Casa editrice: Springer-Verlag New York Inc.
Data di pubblicazione: 2010
Legatura: Brossura
Condizione: New