SOBM - a binary mask for noisy speech that optimises an objective intelligibility metric
Leo Lightburn, Mike Brookes · 2015
It is known that the intelligibility of noisy speech can be improved by applying a binary-valued gain mask to a time-frequency representation of the speech. We present the SOBM, an oracle binary mask that maximises STOI, an objective speech intelligibility metric. We show how to determine the SOBM for a deterministic noise signal and also for a stochastic noise signal with a known power spectrum. We demonstrate that applying the SOBM to noisy speech results in a higher predicted intelligibility than is obtained with other masks and show that the stochastic version is robust to mismatch errors in SNR and noise spectrum.