Feature space generalized variable parameter HMMs for noise robust recognition

Handling variable ambient noise is a challenging task for automatic speech recognition (ASR) systems. To address this issue, multi-style training using speech data collected in diverse noise environments, noise adaptive training or uncertainty decoding techniques can be used. An alternative approach is to explicitly approximate the continuous trajectory of Gaussian component or model space linear transform parameters against the varying noise, for example, using generalized variable parameter HMMs (GVP-HMM). In order to reduce the computational cost of conventional GVP-HMMs when model parameter update against the varying noise condition is required, this paper investigates a novel and more efficient extension of GVPHMMs that can also model the trajectories of feature space linear transforms. Significant error rate reductions of 9.3% and 18.5% relative were obtained over the multi-style training baseline system on Aurora 2 and a medium vocabulary Mandarin Chinese speech recognition task respectively.