Mon-1-2-10 A Space-and-Speaker-Aware Iterative Mask Estimation Approach to Multi-channel Speech Recognition in the CHiME-6 Challenge

Abstract: We propose a space-and-speaker-aware iterative mask estimation (SSA-IME) approach to improving complex angular central Gaussian distributions (cACGMM) based beamforming in an iterative manner by leveraging upon the complementary information obtained from SSA-based regression. First, a mask calculated by beamformed speech features is proposed to enhance the estimation accuracy of the ideal ratio mask from noisy speech. Second, the outputs of cACGMM-beamformed speech with given time annotation as initial values are used to extract the log-power spectral and inter-phase difference features of different speakers serving as inputs to estimate the regression-based SSA model. Finally, in decoding, the mask estimated by the SSA model is also used to iteratively refine cACGMM-based masks, yielding enhanced multi-array speech. Tested on the recent CHiME-6 Challenge Track 1 tasks, the proposed SSA-IME framework significantly and consistently outperforms state-of-the-art approaches, and achieves the lowest word error rates for both Track 1 speech recognition tasks.