Minimum classification error/eigenvoices training for speaker identification

This paper describes a new training approach based on two different techniques (minimum classification error and eigenvoices), in order to achieve a better robustness when only poor training data is provided. In the first two sections of this paper we describe the MCE training and the eigenvoice approach. Then a unified MCE/eigenvoice training algorithm is proposed describing theoretical advantages. We compare the proposed method with classical ML/eigenvoice methods for a speaker identification task. The identification rate improvement is huge for sparse training data (up to 50% in the best case).