SVM based speaker selection using GMM supervector for rapid speaker adaptation

Authors:
Jian Wang;Jianjun Lei;Jun Guo;Zhen Yang
Affiliations:
School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

Citing 2
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Abstract

In this paper, we propose a novel method for rapid speaker adaptation called speaker support vector selection (SSVS). By taking gaussian mixture model (GMM) as speaker model, the speakers acoustically close to the test speaker are selected .Different from other selection method, just computing the likelihood between models, we utilizing support vector machines (SVM) to obtain a ‘more optimal speaker subset'. Such selection is dynamically determined according to the distribution of reference speakers close the test. Furthermore, a single-pass re-estimation procedure conditioned on the selected speakers is shown. This adaptation strategy was evaluated in a large vocabulary speech recognition task. The presented method improves the relative accuracy rates by 13% compared to the baseline system.