Robust features for speech recognition systems

Abstract

In this paper we propose a set of features based on group delay spectrum for speech recognition systems. These features appear to be more robust to channel variations and environmental changes compared to features based on Melspectral coefficients. The main idea is to derive cepstrumlike features from group delay spectrum instead of deriving them from power spectrum. The group delay spectrum is computed from modified auto-correlation-like function. The effectiveness of the new feature set is demonstrated by the results of both speaker-independent (SI) and speaker-dependent (SD) recognition tasks. Preliminary results indicate that using the new features, we can obtain results comparable to Mel cepstra and PLP cepstra in most of the cases and a slight improvement in noisy cases. More optimization of the parameters is needed to fully exploit the nature of the new features.