A PRACTICAL PERCEPTUAL FREQUENCY AUTOREGRESSIVE HMM ENHANCEMENT SYSTEM
B. T. Logan and A. J. Robinson
We have previously developed a speech enhancement scheme which can adapt to unknown additive noise. We model speech and noise using perceptual frequency or `warped' autoregressive HMMs (AR-HMMs) and estimate the clean speech and noise parameters within this framework. In this current work, we investigate the use of our system as a front end to a MFCC recognition system trained on clean speech. To use our system as a front end, we make two modifications. First, we use minimum mean squared error (MMSE) spectral rather than time domain estimators for enhancement. Second, for computational reasons, we form estimators from non-warped AR-HMMs. To avoid mismatch introduced when converting between warped and non-warped models, we use parallel sets of models.
Results are presented for small and medium vocabulary tasks. On the simple task, we are able to approach the performance of a matched system when language model information is included. On the second task, we are not able to incorporate a language model due to modelling deficiencies in AR-HMMs. However, we still demonstrate substantial improvements over baseline results.
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