Abstract:
In developing acoustic parameters based on phonetic features for speaker-independent speech recognition, it is important that the parameters (1) be based on relative measures as opposed to absolute ones to minimize speaker-dependent effects and (2) be selected from a pool of candidates according to some ``goodness'' criteria. In this study, place-of-articulation phonetic features are targeted to help classify obstruent sounds. It will be shown how acoustic phonetic knowledge and statistical analysis were combined to go from qualitative definitions of the acoustic correlates for phonetic features to computational algorithms for extraction of the relevant acoustic properties using discriminant analysis and classification trees. Further, it will be shown how using relative measures reduces speaker-dependent effects, specifically gender, on the acoustic parameters while honing on the phonetic information contained in the speech signal. [Work supported by NSF Research Grant No. IRI-9310518.]