SOBM - a binary mask for noisy speech that optimises an objective intelligibility metric

Leo Lightburn, Mike Brookes
2015 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
It is known that the intelligibility of noisy speech can be improved by applying a binary-valued gain mask to a timefrequency 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
more » ... han is obtained with other masks and show that the stochastic version is robust to mismatch errors in SNR and noise spectrum.
doi:10.1109/icassp.2015.7178938 dblp:conf/icassp/LightburnB15 fatcat:6ffrcm56infpnahp34e5pexii4