A Symmetric Kernel Partial Least Squares Framework for Speaker Recognition

B. V. Srinivasan, Yuancheng Luo, D. Garcia-Romero, D. N. Zotkin, R. Duraiswami
2013 IEEE Transactions on Audio, Speech, and Language Processing  
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 ivector 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
more » ... LS) 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.
doi:10.1109/tasl.2013.2253096 fatcat:usebv7u2i5aflm2b6mi5325uma