Robust Recognition Of Reverberant And Noisy Speech Using Coherence-Based Processing

This paper describes a combination of techniques for improving speech recognition accuracy using two microphones in reverberant and noisy environments. These techniques include both monaural and binaural processing. The first stage is monaural precedence-based processing that enhances the onsets of the incoming speech signal, and hence suppresses later components that are more affected by reverberation. Onset enhancement has been shown to be useful to the human auditory system in separating the direct field from the reverberant field in reverberant environments. The second stage applies emphasis or suppression to signal components based on an estimation of the inter-microphone coherence of the incoming speech signal. Specifically, portions of the speech signal that are less coherent are suppressed, which is intended to reduce the contributions of components that are dominated by diffuse noise or high degrees of reverberation in the input signal. A combination of these techniques is shown to lead to significant improvements in speech recognition accuracy. A DNN-based automatic speech recognition system was used to evaluate the techniques described in this study over a range of reverberation times and signal-to-interferer ratios.