Abstract
We propose a method to estimate the short term predictor (STP) and the long-term predictor (LTP) under noisy conditions. We assume the speech signal to be a single, dual or triple frame asymptotic mean stationary process. The a priori STP parameter distribution is represented as databases sampled from the speech training data. Stochastic integration is used to obtain the minimum mean square error estimates of the STP parameters. After computing the STP parameters, the LTP parameters from a database of pairs of taps and excitation variances are matched, together with the lag, using a likelihood criterion, to the noisy speech. The estimated STP and LTP parameters are also applied to obtain clean speech estimates by means of a Wiener or a Kalman filter. For car noise with an SNR of -5dB, the proposed enhancement method gives a mean opinion score of 3.3 as measured using the perceptual speech quality measure software.
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