A comparison of front-end configurations for robust speech recognition

This paper presents a comparative analysis of the processing stages involved in feature extraction for speech recognition. Feature extraction is considered as comprising three different processing stages; namely static feature extraction, normalisation and inclusion of temporal information. In each stage a comparison of techniques is made, both theoretically and in terms of their comparative performance. The analysis shows that while some techniques may appear significantly different, upon analysis the effect they have on the signal can be similar. Comparative studies include MFCC and PLP analysis, RASTA filtering and cepstral mean normalisation, and temporal derivatives and cepstral-time matrices. Experimental results, on an unconstrained monophone task, compare recognition performance using different front-end configurations.