An elitist approach to automatic articulatory-acoustic feature classification 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 classification in spoken language. The ‘‘elitist’’ approach provides a principled means of selecting frames for which multi-layer perceptron, 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. 2005 Elsevier B.V. All rights reserved.