Unvoiced speech segregation based on CASA and spectral subtraction

Unvoiced speech separation is an important and challenging problem that has not received much attention. We propose a CASA based approach to segregate unvoiced speech from nonspeech interference. As unvoiced speech does not contain periodic signals, we first remove the periodic portions of a mixture including voiced speech. With periodic components removed, the remaining interference becomes more stationary. We estimate the noise energy in unvoiced intervals on the basis of segregated voiced speech. Spectral subtraction is employed to extract time-frequency segments in unvoiced intervals, and we group the segments dominated by unvoiced speech by simple thresholding or Bayesian classification. Systematic evaluation and comparison show that the proposed method considerably improves the unvoiced speech segregation performance under various SNR conditions.