Combining stochastic average gradient and Hessian-free optimization for sequence training of deep neural networks

Abstract

Minimum phone error (MPE) training of deep neural networks (DNN) is an effective technique for reducing word error rate of automatic speech recognition tasks. This training is often carried out using a Hessian-free (HF) quasi-Newton approach, although other methods such as stochastic gradient descent have also been applied successfully. In this paper we present a novel stochastic approach to HF sequence training inspired by recently proposed stochastic average gradient (SAG) method. SAG reuses gradient information from past updates, and consequently simulates the presence of more training data than is really observed for each model update. We extend SAG by dynamically weighting the contribution of previous gradients, and by combining it to a stochastic HF optimization. We term the resulting procedure DSAG-HF. Experimental results for training DNNs on 1500h of audio data show that compared to baseline HF training, DSAG-HF leads to better held-out MPE loss after each model parameter update, and converges to an overall better loss value. Furthermore, since each update in DSAG-HF takes place over smaller amount of data, this procedure converges in about half the time as baseline HF sequence training.