Modelling uncertainty in stochastic vector mapping with minimum classification error training for robust speech recognition

We have witness several works of considering the uncertainty of feature compensation module for robust speech recognition. In most of these studies, the modelling and the exploiting of the uncertainty are seldom treated in a unified way. In this paper, we present a new framework, which casts the problem of considering the uncertainty of feature compensation module as the one of designing a new discriminant function, thus the uncertainty parameters of the feature compensation module and other parameters of the discriminant function can be estimated jointly under a consistent criterion of minimum classification error (MCE). It is hoped that such MCE-trained discriminant function can improve the performance of a maximum discriminant function based speech recognition system. The preliminary experimental results on Aurora2 multi-condition tasks have confirmed the above conjecture.