Robust speech recognition by integrating speech separation and hypothesis testing
Missing-data methods attempt to improve robust speech recognition by distinguishing between reliable and unreliable data in the timefrequency (TF) domain. Such methods require a binary mask to label speech-dominant TF regions of a noisy speech signal as reliable and the rest as unreliable. Current methods for computing the mask are based mainly on bottom-up cues such as harmonicity and produce labeling errors that degrade recognition performance. In this paper, we propose a two-stage recognition system that combines bottom-up and top-down cues in order to simultaneously improve both mask estimation and recognition accuracy. First, an n-best lattice consistent with a speech separation mask is generated. The lattice is then re-scored by expanding the mask using a model-based hypothesis test to determine the reliability of individual TF units. Systematic evaluations of the proposed system show significant improvement in recognition performance compared to that using speech separation alone.
robust speech recognition missing-data recognizer ideal binary mask speech segregation top-down processing