Approximate Decision Function and Optimization for GMM-UBM Based Speaker Verification

Xiang XIAO  Xiang ZHANG  Haipeng WANG  Hongbin SUO  Qingwei ZHAO  Yonghong YAN  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.9   pp.1798-1802
Publication Date: 2009/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.1798
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Speech and Hearing
Keyword: 
automatic speaker verification,  contribution weight re-estimation,  optimization,  

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Summary: 
The GMM-UBM framework has been proved to be one of the most effective approaches to the automatic speaker verification (ASV) task in recent years. In this letter, we first propose an approximate decision function of traditional GMM-UBM, from which it is shown that the contribution to classification of each Gaussian component is equally important. However, research in speaker perception shows that a different speech sound unit defined by Gaussian component makes a different contribution to speaker verification. This motivates us to emphasize some sound units which have discriminability between speakers while de-emphasize the speech sound units which contain little information for speaker verification. Experiments on 2006 NIST SRE core task show that the proposed approach outperforms traditional GMM-UBM approach in classification accuracy.