Automatic model complexity control for generalized variable parameter HMMs

Abstract

An important task for speech recognition systems is to handle the mismatch against a target environment introduced by acoustic factors such as variable ambient noise. To address this issue, it is possible to explicitly approximate the continuous trajectory of optimal, well matched model parameters against the varying noise using, for example, using generalized variable parameter HMMs (GVP-HMM). In order to improve the generalization and computational efficiency of conventional GVP-HMMs, this paper investigates a novel model complexity control method for GVP-HMMs. The optimal polynomial degrees of Gaussian mean, variance and model space linear transform trajectories are automatically determined at local level. Significant error rate reductions of 20% and 28% relative were obtained over the multi-style training baseline systems on Aurora 2 and a medium vocabulary Mandarin Chinese speech recognition task respectively. Consistent performance improvements and model size compression of 57% relative were also obtained over the baseline GVP-HMM systems using a uniformly assigned polynomial degree.