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Improved Methods for Characterizing the Alternative Hypothesis using Minimum Verification Error Training for LLR-Based Speaker Verification

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4 Author(s)
Yi-Hsiang Chao ; Institute of Information Science, Academia Sinica, Taipei, Taiwan; Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan. yschao@iis.sinica.edu.tw ; Wei-Ho Tsai ; Hsin-Min Wang ; Ruei-Chuan Chang

Speaker verification based on the log-likelihood ratio (LLR) is essentially a task of modeling and testing two hypotheses: the null hypothesis and the alternative hypothesis. Since the alternative hypothesis involves unknown imposters, it is usually hard to characterize a priori. In this paper, we propose a framework to better characterize the alternative hypothesis with the goal of optimally separating client speakers from imposters. The proposed framework is built on either a weighted arithmetic combination or a weighted geometric combination of useful information extracted from a set of pre-trained anti-speaker models. The parameters associated with the combinations are then optimized using minimum verification error training such that both the false acceptance probability and the false rejection probability are minimized. Our experiment results show that the proposed framework outperforms conventional LLR-based approaches.

Published in:

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  (Volume:4 )

Date of Conference:

15-20 April 2007