Review of Ideal Binary and Ratio Mask Estimation Techniques for Monaural Speech Separation

Abstract

Monaural speech separation is the process of separating the target speech from a noisy speech mixture recorded using single microphone. It can be used in wide range of applications including mobile telephony, hearing aid design and robust automatic speech and speaker recognition (ASR). Recently, researchers use computational auditory scene analysis (CASA) technique to successfully separate the target speech from the monaural noisy speech mixture. In CASA based monaural speech separation techniques, Ideal binary mask (IBM) and Ideal ratio mask (IRM) has been proposed as a computational goal to improve the speech intelligibility and speech quality. This paper reviews and reports various research works carried out using CASA techniques with IBM and IRM to improve speech intelligibility and quality. The experimental results show that CASA systems using IBM improves the speech intelligibility and using IRM improves the speech quality.