Boosted mixture learning of Gaussian mixture HMMs for speech recognition

In this paper, we propose a novel boosted mixture learning (BML) framework for Gaussian mixture HMMs in speech recognition. BML is an incremental method to learn mixture models for classification problem. In each step of BML, one new mixture component is calculated according to functional gradient of an objective function to ensure that it is added along the direction to maximize the objective function the most. Several techniques have been proposed to extend BML from simple mixture models like Gaussian mixture model (GMM) to Gaussian mixture hidden Markov model (HMM), including Viterbi approximation to obtain state segmentation, weight decay to initialize sample weights to avoid overfitting, combining partial updating with global updating of parameters and using Bayesian information criterion (BIC) for parsimonious modeling. Experimental results on the WSJ0 task have shown that the proposed BML yields relative word and sentence error rate reduction of 10.9% and 12.9%, respectively, over the conventional training procedure.