Microphone Occlusion Mitigation for Own-Voice Enhancement in Head-Worn Microphone Arrays Using Switching-Adaptive Beamforming

Enhancing the user's own-voice for head-worn microphone arrays is an important task in noisy environments to allow for easier speech communication and user-device interaction. However, a rarely addressed challenge is the change of the microphones' transfer functions when one or more of the microphones gets occluded by skin, clothes or hair. The underlying problem for beamforming-based speech enhancement is the (potentially rapidly) changing transfer functions of both the own-voice and the noise component that have to be accounted for to achieve optimal performance. In this paper, we address the problem of an occluded microphone in a head-worn microphone array. We investigate three alternative mitigation approaches by means of (i) conventional adaptive beamforming, (ii) switching between a-priori estimates of the beamformer coefficients for the occluded and unoccluded state, and (iii) a hybrid approach using a switching-adaptive beamformer. In an evaluation with real-world recordings and simulated occlusion, we demonstrate the advantages of the different approaches in terms of noise reduction, own-voice distortion and robustness against voice activity detection errors.