Kernelized Renyi distance for speaker recognition
Perceptual Interfaces and Reality Laboratory, Institute for Advanced Computer Studies (UMIACS), University of Maryland, College Park, MD 20742, USA
IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010
@inproceedings{srinivasan2010kernelized,
title={Kernelized R{‘e}nyi distance for speaker recognition},
author={Srinivasan, V. and Duraiswami, R. and Zotkin, D.N.},
booktitle={Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on},
pages={4506–4509},
year={2010},
organization={IEEE}
}
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 Renyi entropy. The proposed entropic distance, the Kernelized Renyi 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.
July 4, 2011 by hgpu
