SEMI-SUPERVISED SPEAKER IDENTIFICATION UNDER COVARIATE SHIFT

抄録

In this paper, we propose a novel semi-supervised speaker identification method that can alleviate the influence of nonstationarity such as time-dependent voice quality variation, the recording environment change, and speaker feeling. 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 crossvalidation 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 voice quality.