Covariate shift adaptation for semi-supervised speaker identification

Authors:
Makoto Yamada;Masashi Sugiyama;Tomoko Matsui
Affiliations:
Department of Computer Science, Tokyo Institute of Technology, Japan;Department of Computer Science, Tokyo Institute of Technology, Japan;Department of Statistical Modeling, The Institute of Statistical Mathematics, Japan
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

In this paper, we propose a novel semi-supervised speaker identification method that can alleviate the influence of non-stationarity such as session dependent variation, the recording environment change, and physical condition/emotion. We assume that the utterance variation follows the covariate shift model, where only the utterance sample distribution changes in the training and test phases. Our method consists of weighted versions of kernel logistic regression and cross-validation and is theoretically shown to have the capability of alleviating the influence of covariate shift. We experimentally show through text-independent speaker identification simulations that the proposed method is promising in dealing with variations in session dependent utterance variation.