NOISE ESTIMATION FOR ENHANCEMENT AND RECOGNITION WITHIN AN AUTOREGRESSIVE HIDDEN-MARKOV-MODEL FRAMEWORK
B. T. Logan and A. J. Robinson
This paper describes a new algorithm to enhance and recognise noisy speech when only the noisy signal is available. The system uses autoregressive hidden Markov models (HMMs) to model the clean speech and noise and combines these to form a model for the noisy speech. The combined model is used to determine the likelihood of each observation being just noise. These likelihoods are used to weight each observation to form a new estimate of the noise and the process is repeated. Enhancement is performed using Wiener filters formed from the clean speech and noise models. Results are presented for additive stationary Gaussian and coloured noise.
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