Multilevel sampling and aggregation for discriminative training

We propose to use data sampling in the extended Baum-Welch (EBW) algorithm for maximum mutual information (MMI) based estimation of speech acoustic models, and to randomize the configurations of the sampled training sets and aggregate the numerator and denominator sufficient statistics for improving model robustness. We further combine data sampling based ensemble acoustic modeling with the data sampling based EBW, forming a two-level data sampling mechanism for acoustic model training. We conducted experiments on a telehealth conversational speech recognition task, where the two-level data sampling mechanism gave a statistically significant, absolute word accuracy gain of 3.56% over the conventional MMI baseline, corresponding to a 19.44% relative word error rate reduction.