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INTERSPEECH
2010

Bayesian speaker recognition using Gaussian mixture model and laplace approximation

9 years 1 months ago
Bayesian speaker recognition using Gaussian mixture model and laplace approximation
This paper presents a Bayesian approach for Gaussian mixture model (GMM)-based speaker identification. Some approaches evaluate the speaker score of a test speech utterance using a single data likelihood over the GMM learned by point estimation methods according to the maximum likelihood or maximum a posteriori criteria. In contrast, the Bayesian approach evaluates the score by using the expectation of the data likelihood over the posterior distribution of the model parameters, which is depicted by Bayesian integration. However, as the integration can not be derived analytically, we apply Laplace approximation to the derivations. Theoretically, we show that the proposed Bayesian approach is equivalent to the GMMUBM approach when infinite training data is available for each speaker. The results of speaker identification experiments on the TIMIT corpus show that the proposed Bayesian approach consistently outperforms the GMM-UBM approach under very limited training data conditions, alth...
Shih-Sian Cheng, I-Fan Chen, Hsin-Min Wang
Added 18 May 2011
Updated 18 May 2011
Type Journal
Year 2010
Where INTERSPEECH
Authors Shih-Sian Cheng, I-Fan Chen, Hsin-Min Wang
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