Meeting Recognition with Asynchronous Distributed Microphone Array Using Block-Wise Refinement of Mask-Based MVDR Beamformer

This paper addresses a front-end system for speech recognition of spontaneous conversational speech signals that are recorded with asynchronous distributed microphones such as smartphones. In our previous work, we proposed combining blind synchronization and a state-of-the-art microphone array speech enhancement technique, e.g., a time-frequency mask based minimum variance distortionless response (MVDR) beamformer. This approach has provided reasonably high recognition performance even if we use asynchronous microphones. However, because the previous speech enhancement method was applied in a full-batch mode, it has been difficult to track speaker position movement in a real meeting conversation. To make it possible to handle the speaker movement, this paper describes our attempt to refine the mask-based MVDR beamformer in a blockwise manner, and reports that such a refinement reduces the word error rate from 31.4% to 28.8% for real meeting recordings.