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).
Minimum classification error /eigenvoices training for speaker identification
ICASSP 2003, 28th IEEE International Conference on Acoustics, Speech, and Signal Processing, April 6-10, 2003 - Hong Kong
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