Speech enhancement usinga modulation domain Kalman filter post-processor with a Gaussian Mixture noise model

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. We show that the prediction residual signal of the spectral amplitude errors at the output of the baseline MMSE enhancer do not follow a Gaussian distribution. Accordingly, the Kalman filter used in our enhancement algorithm combines a colored noise model with a Gaussian mixture model of the residual noise. 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 and over a previously proposed Kalman filter post-processor.