TAILIEUCHUNG - Báo cáo hóa học: " Stochastic Feature Transformation with Divergence-Based Out-of-Handset Rejection for Robust Speaker Verification"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Stochastic Feature Transformation with Divergence-Based Out-of-Handset Rejection for Robust Speaker Verification | EURASIP Journal on Applied Signal Processing 2004 4 452-465 2004 Hindawi Publishing Corporation Stochastic Feature Transformation with Divergence-Based Out-of-Handset Rejection for Robust Speaker Verification Man-Wai Mak Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong Email enmwmak@ Chi-Leung Tsang Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong Email cltsang@ Sun-Yuan Kung Department of Electrical Engineering Princeton University NJ 08544 USA Email kung@ Received 7 October 2002 Revised 20 June 2003 The performance of telephone-based speaker verification systems can be severely degraded by linear and nonlinear acoustic distortion caused by telephone handsets. This paper proposes to combine a handset selector with stochastic feature transformation to reduce the distortion. Specifically a Gaussian mixture model GMM -based handset selector is trained to identity the most likely handset used by the claimants and then handset-specific stochastic feature transformations are applied to the distorted feature vectors. This paper also proposes a divergence-based handset selector with out-of-handset OOH rejection capability to identity the unseen handsets. This is achieved by measuring the Jensen difference between the selector s output and a constant vector with identical elements. The resulting handset selector is combined with the proposed feature transformation technique for telephone-based speaker verification. Experimental results based on 150 speakers of the HTIMIT corpus show that the handset selector either with or without OOH rejection capability is able to identify the seen handsets accurately in both cases . Results also demonstrate that feature transformation performs significantly better than the classical cepstral

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