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Speaker adaptation techniques for speech recognition using probabilistic models.
- Source :
- Electronics & Communications in Japan, Part 3: Fundamental Electronic Science; Dec2005, Vol. 88 Issue 12, p25-42, 18p
- Publication Year :
- 2005
-
Abstract
- In speech recognition, speaker adaptation refers to the range of techniques whereby a speech recognition system is adapted to the acoustic features of a specific user using a small sample of utterances from that user. In recent years the practical development of speaker-independent speech recognition systems using continuous density hidden Markov models has seen significant progress; however, the recognition performance of these systems has not yet reached that of speaker-dependent speech recognition systems in which a user's speech is registered beforehand. Much hope has therefore been placed on the establishment of speaker adaptation techniques that can bring performance of a speaker-independent system up to that of a speaker-dependent one using the smallest amounts of data. In this paper we present a survey of previous research into speaker adaptation techniques focusing particularly on three important approaches in this area: maximum a posteriori (MAP) parameter estimation, maximum likelihood linear regression (MLLR), and eigenvoices. We also discuss approaches that combine these techniques in a lateral fashion. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 88(12): 25–42, 2005; Published online in Wiley InterScience (<URL>www.interscience.wiley.com</URL>). DOI 10.1002/ ecjc.20207 [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10420967
- Volume :
- 88
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Electronics & Communications in Japan, Part 3: Fundamental Electronic Science
- Publication Type :
- Academic Journal
- Accession number :
- 17454495
- Full Text :
- https://doi.org/10.1002/ecjc.20207