Combining Binaural and Cortical Features for Robust Speech Recognition

Abstract

The segregation of concurrent speakers and other sound sources is an important ability of the human auditory system, but is missing in most current systems for automatic speech recognition ASR, resulting in a large gap between human and machine performance. This study combines processing related to peripheral and cortical stages of the auditory pathway: A physiologically motivated binaural model estimates the positions of moving speakers to enhance the desired speech signal. Second, signals are converted to spectro-temporal Gabor features that resemble cortical speech representations and which have been shown to improve ASR in noisy conditions. Spectro-temporal Gabor features improve recognition results in all acoustic conditions under consideration compared with Mel-frequency cepstral coefficients. Binaural processing results in lower word error rates WERs in acoustic scenes with a concurrent speaker, whereas monaural processing should be preferred in the presence of a stationary masking noise. In-depth analysis of binaural processing identifies crucial processing steps such as localization of sound sources and estimation of the beamformer's noise coherence matrix, and shows how much each processing step affects the recognition performance in acoustic conditions with different complexity.