Tracking speech-presence uncertainty to improve speech enhancement in non-stationary noise environments

Speech enhancement algorithms which are based on estimating the short-time spectral amplitude of the clean speech have better performance when a soft-decision gain modification, depending on the a priori probability of speech absence, is used. In reported works a fixed probability, q, is assumed. Since speech is non-stationary and may not be present in every frequency bin when voiced, we propose a method for estimating distinct values of q for different bins which are tracked in time. The estimation is based on a decision-theoretic approach for setting a threshold in each bin followed by short-time averaging. The estimated q's are used to control both the gain and the update of the estimated noise spectrum during speech presence in a modified MMSE log-spectral amplitude estimator. Subjective tests resulted in higher scores than for the IS-127 standard enhancement algorithm, when pre-processing noisy speech for a coding application