On the use of weighted filter bank analysis for the derivation of robust MFCCs

Abstract

In this paper, we discuss the use of weighted filter bank analysis (WFBA) to increase the discriminating ability of mel frequency cepstral coefficients (MFCCs). The WFBA emphasizes the peak structure of the log filter bank energies (LFBEs) obtained from filter bank analysis while attenuating the components with lower energy in a simple, direct, and effective way. Experimental results for recognition of continuous Mandarin telephone speech indicate that the WFBA-based cepstral features are more robust than those derived by employing the standard filter bank analysis and some widely used cepstral liftering and frequency filtering schemes both in channel-distorted and noisy conditions.