Discriminative spectral-temporal multiresolution features for speech recognition

Multi-resolution features, which are based on the premise that there may be more cues for phonetic discrimination in a given sub-band than in another, have been shown to outperform the standard MFCC feature set for both classification and recognition tasks on the TIMIT database. This paper presents an investigation into possible strategies to extend these ideas from the spectral domain into both the spectral and temporal domains. Experimental work on the integration of segmental models, which are better at capturing the longer term phonetic correlation of a phonetic unit, into the discriminative multi-resolution framework is presented. Results are presented which show that including this supplementary temporal information offers an improvement performance for the phoneme classification task over the standard multi-resolution MFCC feature set with time derivatives appended. Possible strategies for the extension of theses techniques into the area of continuous speech recognition are discussed