An elitist approach to automatic articulatory-acoustic feature classi cation for phonetic characterization of spoken language

A novel framework for automatic articulatory-acoustic feature extraction has been developed for enhancing the accuracy of placeand manner-of-articulation classi cation in spoken language. The ‘‘elitist’’ approach provides a principled means of selecting frames for which multi-layer perceptron, neural-network classi ers are highly con dent. Using this method it is possible to achieve a frame-level accuracy of 93% on ‘‘elitist’’ frames for manner classi cation 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 classi cation relative to performance for a manner-independent system. A comparable enhancement in classi cation 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 transcriptand 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.