An auditory-based feature for robust speech recognition

A conventional automatic speech recognizer does not perform well in the presence of noise, while human listeners are able to segregate and recognize speech in noisy conditions. We study a novel feature based on an auditory periphery model for robust speech recognition. Specifically, gammatone frequency cepstral coefficients are derived by applying a cepstral analysis on gammatone filterbank responses. Our evaluations show that the proposed feature performs considerably better than conventional acoustic features. We further demonstrate that integrating the proposed feature with a computational auditory scene analysis system yields promising recognition performance.