An elitist approach to automatic articulatory-acoustic feature classification for phonetic characterization of spoken language.

Abstract

A novel framework for automatic articulatory-acoustic feature extraction has been developed for enhancing the accuracy of place- and manner-of-articulation classification in spoken language. The elitist approach provides a principled means of selecting frames for which multi-layer perception, neural-network classifiers are highly confident. Using this method it is possible to achieve a frame-level accuracy of 93% on elitist frames for manner classification on a corpus of American English sentences passed through a telephone network (NTIMIT). Place-of-articulation information is extracted for each manner class independently, resulting in an appreciable gain in place-feature classification relative to performance for a manner-independent system. A comparable enhancement in classification performance for the elitist approach is evidenced when applied to a Dutch corpus of quasi-spontaneous telephone interactions (VIOS). The elitist framework provides a potential means of automatically annotating a corpus at the phonetic level without recourse to a word-level transcript and could thus be of utility for developing training materials for automatic speech recognition and speech synthesis applications, as well as aid the empirical study of spoken language.