Frame-level AnyBoost for LVCSR with the MMI Criterion

Abstract

This paper propose a variant of AnyBoost for a large vocabulary continuous speech recognition (LVCSR) task. AnyBoost is an efficient algorithm to train an ensemble of weak learners by gradient descent for an objective function.We present a novel training procedure that trains acoustic models via the MMI criterion using data that is weighted proportional to the summation of the posterior functions of previous round of weak learners. Optimized for system combination by n-best ROVER at runtime, data weights for a new weak learner are computed as a weighted summation of posteriors of previous weak learners. We compare a frame-based version and a sentence-based version of our proposed algorithm with a frame-based AdaBoost algorithm. We will present results on a voice search task trained with different amounts of data with gains of 5.1% to 7.5% relative in WER can be obtained by three rounds of boosting.