A speech enhancement system based on data clustering and cumulative histogram equalization

We present a data driven noise suppression filtering system which combines the data clustering and the cumulative histogram equalization techniques.This method uses the SNRGMM index, which has been developed in our previous works, for clustering a speech data into sub-data with the same index. Furthermore,for each sub-data, the cumulative histogram equalization filtering is learned on each the subband log-spectral magnitude domain. The case, when a noisy speech data is not available, is also consdirered in this work. For that case the SNRGMM can be used for the very quick and flexible simulation of a noisy speech data and without any loss of quality in the final system. The experimental evaluation on the AURORA2 Japansese version shows the improvement of the proposed system in both SNR and ASR performances.