A unified framework of HMM adaptation with joint compensation of additive and convolutive distortions

In this paper, we present our recent development of a model-domain environment robust adaptation algorithm, which demonstrates high performance in the standard Aurora 2 speech recognition task. The algorithm consists of two main steps. First, the noise and channel parameters are estimated using multi-sources of information including a nonlinear environment- distortion model in the cepstral domain, the posterior probabilities of all the Gaussians in speech recognizer, and truncated vector Taylor series (VTS) approximation. Second, the estimated noise and channel parameters are used to adapt the static and dynamic portions (delta and delta–delta) of the HMM means and variances. This two-step algorithm enables joint compensation of both additive and convolutive distortions (JAC). The hallmark of our new approach is the use of a nonlinear, phase-sensitive model of acoustic distortion that captures phase asynchrony between clean speech and the mixing noise. In the experimental evaluation using the standard Aurora 2 task, the proposed Phase-JAC/VTS algorithm achieves 93.32% word accuracy using the clean-trained complex HMM backend as the baseline system for the unsupervised model adaptation. This represents high recognition performance on this task without discriminative training of the HMM system. The experimental results show that the phase term, which was missing in all previous HMM adaptation work, contributes significantly to the achieved high recognition accuracy.