Weighted matching algorithms and reliability in noise cancelling by spectral subtraction

This paper addresses the problem of speech recognition with signals corrupted by additive noise at moderate SNR. A technique based on spectral subtraction and noise cancellation reliability weighting in acoustic pattern matching algorithms is studied. A model for additive noise is proposed and used to compute the variance of the hidden clean signal information and the reliability of the spectral subtraction process. The results presented show that a proper weight on the information provided by static parameters can substantially reduce the error rate.