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Incorporating Knowledge Sources into Statistical Speech Recognition de Sakriani Sakti

Incorporating Knowledge Sources into Statistical Speech Recognition: Lecture Notes in Electrical Engineering, cartea 42

Autor Sakriani Sakti, Konstantin Markov, Satoshi Nakamura, Wolfgang Minker
en Limba Engleză Hardback – 19 mar 2009
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.
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Specificații

ISBN-13: 9780387858296
ISBN-10: 0387858296
Pagini: 196
Ilustrații: XXIV, 196 p. 100 illus.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.43 kg
Ediția:2009
Editura: Springer Us
Colecția Springer
Seria Lecture Notes in Electrical Engineering

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

and Book Overview.- Statistical Speech Recognition.- Graphical Framework to Incorporate Knowledge Sources.- Speech Recognition Using GFIKS.- Conclusions and Future Directions.

Textul de pe ultima copertă

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.

Caracteristici

Provides an efficient general framework to incorporate additional knowledge sources into state-of-the-art statistical ASR systems Demonstrates applications to existing ASR problems with their respective model-based likelihood functions in flexible ways