Publications

Kernelized Rényi distance for speaker recognition

35th International Conference on Acoustics, Speech and Signal Processing

Published March 14, 2010

Balaji Vasan Srinivasan, R. Duraiswami, D. Zotkin

Speaker recognition systems classify a test signal as a speaker or an imposter by evaluating a matching score between input and reference signals. We propose a new information theoretic approach for computation of the matching score using the Rényi entropy. The proposed entropic distance, the Kernelized Rényi distance (KRD), is formulated in a non-parametric way and the resulting measure is efficiently evaluated in a parallelized fashion on a graphical processor. The distance is then adapted as a scoring function and its performance compared with other popular scoring approaches in a speaker identification and speaker verification framework.

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Research Areas:  AI & Machine Learning Audio