Coupling binary masking and robust ASR

Abstract

We present a novel framework for performing speech separation and robust automatic speech recognition (ASR) in a unified fashion. Separation is performed by estimating the ideal binary mask (IBM), which identifies speech dominant and noise dominant units in a time-frequency (T-F) representation of the noisy signal. ASR is performed on extracted cepstral features after binary masking. Previous systems perform these steps in a sequential fashion -separation followed by recognition. The proposed framework, which we call bidirectional speech decoding (BSD), unifies these two stages. It does this by using multiple IBM estimators each of which is designed specifically for a back-end acoustic phonetic unit (BPU) of the recognizer. The standard ASR decoder is modified to use these IBM estimators to obtain BPU-specific cepstra during likelihood calculation. On the Aurora-4 robust ASR task, the proposed framework obtains a relative improvement of 17% in word error rate over the noisy baseline. It also obtains significant improvements in the quality of the estimated IBM.