Robust speaker identification based on t-distribution mixture model

Authors:
Younjeong Lee;Hernsoo Hahn;Youngjoon Han;Joohun Lee
Affiliations:
School of Electronic Engineering, Soongsil University, Seoul, Korea;School of Electronic Engineering, Soongsil University, Seoul, Korea;School of Electronic Engineering, Soongsil University, Seoul, Korea;Dept. of Internet Broadcasting, Dong-Ah Broadcasting College, Anseong, Korea
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 3
Cited 1

Robust mixture modelling using the t distribution

Statistics and Computing
Robust mixture modelling using multivariate t-distribution with missing information

Pattern Recognition Letters
Robust speaker recognition against utterance variations

ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII

On EM Estimation for Mixture of Multivariate t-Distributions

Neural Processing Letters

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Abstract

To minimize the outliers’ effects, in this paper, a new speaker identification scheme based on the t-distribution mixture model is proposed. Since the t-distribution provides a longer and heavier tailed alternative to the Gaussian distribution, the mixture model with multivariate t-distribution is expected to show more robust results than the Gaussian mixture model(GMM) in the cases where outliers exist. In experiments, we compared the performance of the proposed scheme with that of using the conventional GMM to show its robustness.