Abstract
This paper presents a cluster-based feature transformation technique for telephone-based speaker verification when labels of the handset types are not available during the training phase. The technique combines a cluster selector with cluster-dependent feature transformations to reduce the acoustic mismatches among different handsets. Specifically, a GMM-based cluster selector is trained to identify the cluster that best represents the handset used by a claimant. Handset distorted features are then transformed by cluster-specific feature transformation to remove the acoustic distortion before being presented to the clean speaker models. Experimental results show that cluster-dependent feature transformation with number of clusters larger than the actual number of handsets can achieve a performance level very close to that achievable by the handset-based transformation approaches.
This work was supported by The Hong Kong Polytechnic University Grant No. A442 and HKSAR RGC Grant No. PolyU5129/01E. S.Y. Kung was also a Distinguished Chair Professor of The Hong Kong Polytechnic University.
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Tsang, CL., Mak, MW., Kung, SY. (2003). Cluster-Dependent Feature Transformation for Telephone-Based Speaker Verification. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_11
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DOI: https://doi.org/10.1007/3-540-44887-X_11
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