Discriminative training of GMM based on Maximum Mutual Information for language identification

In this paper, a discriminative training procedure based on maximum mutual information (MMI) for a Gaussian mixture model (GMM) language identification system is described. The idea is to find the model parameters lambda that minimize the conditional entropy H_lambda (C | X) of the random variable C given the random variable X , which means minimize the uncertainty in knowing what language was spoken given access to the utterance in X . The implementation of the proposal is based on the generalized probabilistic descent (GPD) algorithm formulated to estimate the GMM parameters. The evaluation is conducted using the OGI multi-language telephone speech corpus. The experimental results show such system is very effective in language identification tasks