Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
865674 | Tsinghua Science & Technology | 2008 | 5 Pages |
Abstract
Unseen handset mismatch is the major source of performance degradation in speaker identification in telecommunication environments. To alleviate the problem, a maximum likelihood a priori knowledge interpolation (ML-AKI)-based handset mismatch compensation approach is proposed. It first collects a set of handset characteristics of seen handsets to use as the a priori knowledge for representing the space of handsets. During evaluation the characteristics of an unknown test handset are optimally estimated by interpolation from the set of the a priori knowledge. Experimental results on the HTIMIT database show that the ML-AKI method can improve the average speaker identification rate from 60.0% to 74.6% as compared with conventional maximum a posteriori-adapted Gaussian mixture models. The proposed ML-AKI method is a promising method for robust speaker identification.
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Authors
Yuanfu (å»å
ç«), Zhixian (åºæºæ¾), Jyhher (æ¨æºå),