VTS

The VTS approach for noise reduction is based on a statistical for-mulation. It provides the expected value of the clean speech given the noisy observations and statistical models for the clean speech and the additive noise. The compensated signal is only an approxi-mation of the clean one and retains a residual mismatch. The main objective of this work is to characterize this residual noise and to propose techniques to reduce its unwanted effects. Two different approaches to this problem are presented in this paper. The first one is based on linear filtering the time sequences of compensated acoustic parameters; for this purpose we use LDA-based RASTA-like FIR filters. The second approach is based on canceling the distortion introduced into the probability distribution of acoustic parameters and uses the well-known technique of histogram equal-ization. Results reported on AURORA database show that the pro-posed methods increase the recognition performance. 1