An auditory system-based feature for robust speech recognition

Abstract

An auditory feature extraction algorithm for robust speech recognition in adverse acoustic environments is presented. The feature computation is comprised of an outer-middle-ear transfer function, FFT, frequency conversion from linear to the Bark scale, auditory filtering, nonlinearity, and discrete cosine transform. The feature is evaluated in two tasks: connected-digit recognition and large vocabulary continuous speech recognition. The tested data were under various noise conditions, including handset and hands-free speech data in landline and wireless communications with additive car and babble noise. Compared with the LPCC, MFCC, MEL-LPCC, and PLP features, the proposed feature has an average 20% to 30% string error rate reduction on the connected-digit task, and 8% to 14% word error rate reduction on the Wall Street Journal task in various additive noise conditions.