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Bayesian adaptation in speech recognition

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3 Author(s)
Brown, P. ; Verbex Corporation, Bedford, MA ; Chin-Hui Lee ; Spohrer, J.

In order to achieve state-of-the-art performance in a speaker-dependent speech recognition task, it is necessary to collect a large number of acoustic data samples during the training process. Providing these samples to the system can be a long and tedious process for users. One way to attack this problem is to make use of extra information from a data bank representing a large population of speakers. In this paper we demonstrate that by using Bayesian techniques, prior knowledge derived from speaker-independent data can be combined with speaker-dependent training data to improve system performance.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '83.  (Volume:8 )

Date of Conference:

Apr 1983