@conference {13952,
	title = {Kernelized R{\'e}nyi distance for speaker recognition},
	booktitle = {Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on},
	year = {2010},
	month = {2010/03//},
	pages = {4506 - 4509},
	abstract = {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 Re $\#$x0301;nyi entropy. The proposed entropic distance, the Kernelized Re $\#$x0301;nyi 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.},
	keywords = {$\#$x0301;nyi, approach;input, distance;reference, entropy;graphical, equipment;entropy;speaker, graphic, identification;speaker, processor;information, Re, recognition;, recognition;speaker, signals;kernelized, signals;speaker, theoretic, verification;computer},
	doi = {10.1109/ICASSP.2010.5495587},
	author = {Vasan Srinivasan,B. and Duraiswami, Ramani and Zotkin,Dmitry N}
}
