Speech enhancement using a robust Kalman filter post-processor in the modulation domain

We propose a speech enhancement algorithm that applies a Kalman filter in the modulation domain to the output of a conventional enhancer operating in the time-frequency domain. The speech model required by the Kalman filter is obtained by performing linear predictive analysis in each frequency bin of the modulation domain signal. We show, however, that the corresponding speech synthesis filter can have a very high gain at low frequencies and may approach instability. To improve the stability of the synthesis filter, we propose two alternative methods of limiting its low frequency gain. We evaluate the performance of the speech enhancement algorithm on the core TIMIT test set and demonstrate that it gives consistent performance improvements over the baseline enhancer.