Discriminative training of GMM for speaker identification

We describe a novel discriminative training procedure for a Gaussian mixture model (GMM) speaker identification system. The proposal is based on the segmental generalized probabilistic descent (GPD) algorithm formulated to estimate the GMM parameters. Two major innovations over similar formulations of segmental GPD training are proposed. (1) A misclassification measure based on an individual representation of competing speakers, that explicitly allows to take into account different learning strategies for correctly or incorrectly classified speakers. (2) An empirical loss function to control the training procedure convergence, with a likelihood-based selection of correctly or incorrectly classified competing speakers. A comparison between the proposed method and the traditional GPD algorithm is also presented.