@conference {13943,
	title = {A partial least squares framework for speaker recognition},
	booktitle = {Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on},
	year = {2011},
	month = {2011/05//},
	pages = {5276 - 5279},
	abstract = {Modern approaches to speaker recognition (verification) operate in a space of "supervectors" created via concatenation of the mean vectors of a Gaussian mixture model (GMM) adapted from a universal background model (UBM). In this space, a number of approaches to model inter-class separability and nuisance attribute variability have been proposed. We develop a method for modeling the variability associated with each class (speaker) by using partial-least-squares - a latent variable modeling technique, which isolates the most informative subspace for each speaker. The method is tested on NIST SRE 2008 data and provides promising results. The method is shown to be noise-robust and to be able to efficiently learn the subspace corresponding to a speaker on training data consisting of multiple utterances.},
	keywords = {approximations;speaker, attribute, background, GMM;Gaussian, least, mixture, model;Gaussian, model;NIST, modeling, processes;least, recognition;, recognition;speaker, separability;latent, squares, squares;partial-least-squares;speaker, SRE;interclass, technique;multiple, utterances;nuisance, variability;partial, variable, verification;universal},
	doi = {10.1109/ICASSP.2011.5947548},
	author = {Srinivasan,B.V. and Zotkin,Dmitry N and Duraiswami, Ramani}
}
@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}
}
