Spectral Subband Centroid Features For Speech Recognition

Abstract

Cepstral coefficients derived either through linear prediction (LP) analysis or from filter banks are perhaps the most commonly used features in currently available speech recognition systems. In this paper, we propose spectral subband centroids as new features and use them as a supplement to cepstral features for speech recognition. We show that these features have properties similar to formant frequencies and they are quite robust to noise. Recognition results are reported, justifying the usefulness of these features as supplementary features.