Overview
- 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
Part of the book series: Lecture Notes in Electrical Engineering (LNEE, volume 42)
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Table of contents (5 chapters)
Keywords
About this book
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.
Bibliographic Information
Book Title: Incorporating Knowledge Sources into Statistical Speech Recognition
Authors: Wolfgang Minker, Satoshi Nakamura, Konstantin Markov, Sakriani Sakti
Series Title: Lecture Notes in Electrical Engineering
DOI: https://doi.org/10.1007/978-0-387-85830-2
Publisher: Springer New York, NY
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag US 2009
Hardcover ISBN: 978-0-387-85829-6Published: 19 March 2009
Softcover ISBN: 978-1-4419-4676-8Published: 05 November 2010
eBook ISBN: 978-0-387-85830-2Published: 27 February 2009
Series ISSN: 1876-1100
Series E-ISSN: 1876-1119
Edition Number: 1
Number of Pages: XXIV, 196
Number of Illustrations: 100 b/w illustrations
Topics: Signal, Image and Speech Processing, Acoustics, Communications Engineering, Networks, Computer Communication Networks, Electrical Engineering