Glottal Model Based Speech Beamforming for ad-hoc Microphone Arrays

We are interested in the task of speech beamforming in conference room meetings, with microphones built in the electronic devices brought and casually placed by meeting participants. This task is challenging because of the inaccuracy in position and interference calibration due to random microphone configuration, variance of microphone quality, reverberation etc. As a result, not many beamforming algorithms perform better than simply picking the closest microphone in this setting. We propose a beamforming called Glottal Residual Assisted Beamforming (GRAB). It does not rely on any position or interference calibration. Instead, it incorporates a source-filter speech model and minimizes the energy that cannot be accounted for by the model. Objective and subjective evaluations on both simulation and real-world data show that GRAB is able to suppress noise effectively while keeping the speech natural and dry. Further analyses reveal that GRAB can distinguish contaminated or reverberant channels and take appropriate action accordingly.