Robust Speaker Recognition Using Binary Time-Frequency Masks

Conventional speaker recognition systems perform poorly under noisy conditions. In this paper, we evaluate binary time-frequency masks for robust speaker recognition. An ideal binary mask is a priori defined as a binary matrix where 1 indicates that the target is stronger than the interference within the corresponding time-frequency unit and 0 indicates otherwise. We perform speaker identification and verification using a missing data recognizer under cochannel and other noise conditions, and show that the ideal binary mask provides large performance gains. By employing a speech segregation system that estimates the ideal binary mask, we achieve significant improvements over alternative approaches. Our study, thus, demonstrates that the use of binary masking represents a promising direction for robust speaker recognition