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A Bayesian approach to speaker adaptation for the stochastic segment model

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3 Author(s)
Necioglu, B.F. ; Boston Univ., MA, USA ; Ostendorf, M. ; Rohlicek, J.R.

Speaker adaptation is frequently used to achieve good speech recognition performance without the high costs associated with training a speaker-dependent model. The main goal of this study is to investigate speaker adaptation for recognizers using multivariate Gaussian densities, specifically, the stochastic segment model. A Bayesian approach is followed, with estimation of the parameters of a speaker-adapted model based on prior densities obtained from speaker-independent data. Experimental results achieve 16% error reduction using mean adaptation with roughly 3 min of speech, nearly half the difference between speaker-independent and speaker-dependent recognition rates

Published in:

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on  (Volume:1 )

Date of Conference:

23-26 Mar 1992