Constraints on ideal binary masking for the perception of spectrally-reduced speech

Abstract

This study investigated recognition of sentences processed using ideal binary masking (IBM) with limited spectral resolution. Local thresholds (LCs) of −12, 0, and 5 dB were applied which altered the target and masker power following IBM. Recognition was reduced due to persistence of the masker and limited target recovery, thus preventing IBM from ideal target-masker segregation. Linear regression and principal component analyses showed that, regardless of masker type and number of spectral channels, higher LCs were associated with poorer recognition. In addition, limitations on target recovery led to more detrimental effects on speech recognition compared to persistence of the masker.