@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 {12482,
	title = {Robust RVM regression using sparse outlier model},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
	year = {2010},
	month = {2010/06//},
	pages = {1887 - 1894},
	abstract = {Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision problems such as age, head pose, 3D human pose and lighting estimation. However, the presence of outliers in the training dataset makes the estimates from these regression techniques unreliable. In this paper, we propose robust versions of the RVM regression that can handle outliers in the training dataset. We decompose the noise term in the RVM formulation into a (sparse) outlier noise term and a Gaussian noise term. We then estimate the outlier noise along with the model parameters. We present two approaches for solving this estimation problem: (1) a Bayesian approach, which essentially follows the RVM framework and (2) an optimization approach based on Basis Pursuit Denoising. In the Bayesian approach, the robust RVM problem essentially becomes a bigger RVM problem with the advantage that it can be solved efficiently by a fast algorithm. Empirical evaluations, and real experiments on image de-noising and age estimation demonstrate the better performance of the robust RVM algorithms over that of the RVM reg ression.},
	keywords = {3D, analysis;, approach;Gaussian, denoising;computer, denoising;lighting, denoising;regression, estimation;relevance, human, machine;robust, model;Gaussian, noise;basis, noise;computer, outlier, pose;Bayesian, pursuit, regression;sparse, RVM, vector, vision;image},
	doi = {10.1109/CVPR.2010.5539861},
	author = {Mitra, K. and Veeraraghavan,A. and Chellapa, Rama}
}
