An Elitist Approach to Articulatory-Acoustic Feature Classification

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 focuses on frames for which neural network (MLP) classifiers are highly confident, and discards the rest. Using this method, it is possible to achieve a frame-level accuracy of 93% for manner information on a corpus of American English sentences passed through a telephone network (NTIMIT). Place 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. 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.