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A Symmetric Kernel Partial Least Squares Framework for Speaker Recognition

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5 Author(s)
Srinivasan, B.V. ; Adobe Res. Bangalore Labs., Bangalore, India ; Yuancheng Luo ; Garcia-Romero, D. ; Zotkin, D.N.
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I-vectors are concise representations of speaker characteristics. Recent progress in i-vectors related research has utilized their ability to capture speaker and channel variability to develop efficient automatic speaker verification (ASV) systems. Inter-speaker relationships in the i-vector space are non-linear. Accomplishing effective speaker verification requires a good modeling of these non-linearities and can be cast as a machine learning problem. Kernel partial least squares (KPLS) can be used for discriminative training in the i-vector space. However, this framework suffers from training data imbalance and asymmetric scoring. We use “one shot similarity scoring” (OSS) to address this. The resulting ASV system (OSS-KPLS) 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.

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Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:21 ,  Issue: 7 )