Sequence-discriminative training of deep neural networks

Abstract

Sequence-discriminative training of deep neural networks (DNNs) is investigated on a standard 300 hour American En- glish conversational telephone speech task. Different sequence- discriminative criteria — maximum mutual information (MMI), minimum phone error (MPE), state-level minimum Bayes risk (sMBR), and boosted MMI — are compared. Two different heuristics are investigated to improve the performance of the DNNs trained using sequence-based criteria — lattices are re- generated after the first iteration of training; and, for MMI and BMMI, the frames where the numerator and denominator hy- potheses are disjoint are removed from the gradient compu- tation. Starting from a competitive DNN baseline trained us- ing cross-entropy, different sequence-discriminative criteria are shown to lower word error rates by 7-9% relative, on aver- age. Little difference is noticed between the different sequence- based criteria that are investigated. The experiments are done using the open-source Kaldi toolkit, which makes it possible for the wider community to reproduce these results. Index Terms: speech recognition, deep learning, sequence- criterion training, neural networks, reproducible research