Semi-supervised speaker identification under covariate shift

Authors:
Makoto Yamada;Masashi Sugiyama;Tomoko Matsui
Affiliations:
Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Okayama, Tokyo 152-8552, Japan and Department of Statistical Science, The Graduate University for Advanced Studies, 4-6-7 Mina ...;Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Okayama, Tokyo 152-8552, Japan;Department of Statistical Modeling, The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan
Venue:
Signal Processing
Year:
2010

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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 conditions/emotions. We assume that the voice quality variants follow the covariate shift model, where only the voice feature 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/dependent speaker identification simulations that the proposed method is promising in dealing with variations in voice quality.