ASR-driven top-down binary mask estimation using spectral priors

Typical mask estimation algorithms use low-level features to estimate the interfering noise or instantaneous SNR. We propose a simple top-down approach to mask estimation. The estimated mask is based on a specific hypothesis of the underlying speech without using information about the interference or the instantaneous SNR. In this pilot study, we observe a 9% reduction in word error over a baseline recognition system on the Aurora4 corpus, though much greater gains could theoretically be achieved through improvements to the model selection process. We also present SNR improvement results showing our method performs as well as a standard MMSE-based method, demonstrating that speech recognition can aid speech enhancement. Thus, the relationship between recognition and enhancement need not be one way: linguistic information can play a significant role in speech enhancement.