About this book
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.
- Book Title Incorporating Knowledge Sources into Statistical Speech Recognition
- Series Title Lecture Notes in Electrical Engineering
- DOI https://doi.org/10.1007/978-0-387-85830-2
- Copyright Information Springer Science+Business Media, LLC 2009
- Publisher Name Springer, Boston, MA
- eBook Packages Engineering Engineering (R0)
- Hardcover ISBN 978-0-387-85829-6
- Softcover ISBN 978-1-4419-4676-8
- eBook ISBN 978-0-387-85830-2
- Series ISSN 1876-1100
- Series E-ISSN 1876-1119
- Edition Number 1
- Number of Pages XXIV, 196
- Number of Illustrations 100 b/w illustrations, 0 illustrations in colour
Signal, Image and Speech Processing
Communications Engineering, Networks
Computer Communication Networks
- Buy this book on publisher's site