@conference {13947,
	title = {Kernel partial least squares for speaker recognition},
	booktitle = {Twelfth Annual Conference of the International Speech Communication Association},
	year = {2011},
	month = {2011///},
	abstract = {I-vectors are a concise representation of speaker characteristics. Recent advances in speaker recognition have utilized their ability to capture speaker and channel variability to develop efficient recognition engines. Inter-speaker relationships in the i-vector space are non-linear. Accomplishing effective speaker recognition requires a good modeling of these non-linearities and can be cast as a machine learning problem. In this paper, we propose a kernel partial least squares (kernel PLS, or KPLS) framework for modeling speakers in the i-vectors space. The resulting recognition system is tested across several conditions of the NIST SRE 2010 extended core data set and compared against state-of-the-art systems: Joint Factor Analysis (JFA), Probabilistic Linear Discriminant Analysis (PLDA), and Cosine Distance Scoring (CDS) classifiers. Improvements are shown.},
	author = {Srinivasan,B.V. and Garcia-Romero,D. and Zotkin,Dmitry N and Duraiswami, Ramani}
}
