Spectral Moment Features Augmented by Low Order Cepstral Coefficients for Robust ASR

We propose a novel Automatic Speech Recognition (ASR) front-end, that consists of the first central Spectral Moment time-frequency distribution Augmented by low order Cepstral coefficients (SMAC). We prove that the first central spectral moment is proportional to the spectral derivative with respect to the filter's central frequency. Consequently, the spectral moment is an estimate of the frequency domain derivative of the speech spectrum. However information related to the entire speech spectrum, such as the energy and the spectral tilt, is not adequately modeled. We propose adding this information with few cepstral coefficients. Furthermore, we use a mel-spaced Gabor filterbank with 70% frequency overlap in order to overcome the sensitivity to pitch harmonics. The novel SMAC front-end was evaluated for the speech recognition task for a variety of recording conditions. The experimental results have shown that SMAC performs at least as well as the standard MFCC front-end in clean conditions, and significantly outperforms MFCCs in noisy conditions.