Syllable Classification Using Articulatory-Acoustic Features

Abstract

This paper investigates the use of articulatory-acoustic features for the classification of syllables in TIMIT. The main motivation for this study is to circumvent the "beads-on-a-string" problem, i.e. the assumption that words can be described as a simple concatenation of phones. Posterior probabilities for articulatory-acoustic features are obtained from artificial neural nets and are used to classify speech within the scope of syllables instead of phones. This gives the opportunity to account for asynchronous feature changes, exploiting the strengths of the articulatory-acoustic features, instead of losing the potential by reverting to phones.