A Soft Masking Strategy Based on Multichannel Speech Probability Estimation for Source Separation and Robust Speech Recognition

In this paper, we present a post processing algorithm that improves the quality of the results of ICA-algorithms by applying a modified speech enhancement technique. The proposed method is based on estimating speech probabilities from the ICA outputs by means of two dimensional correlations. With these probabilities, a soft masking function can be applied on the ICA outputs, which results in significantly increased interferer suppression. In order to avoid negative influences on subsequent speech recognition, missing feature recognition has been applied to robustly recognize the non-linearly processed speech signal. The algorithm has been tested on real-room speech mixtures with a reverberation time of 300ms, where an SIR-improvement of up to 32dB has been obtained, which was 10dB above ICA performance for the same dataset.