Machine Learning Techniques for the Identification of Cues for Stop Place

Abstract

This paper is situated in a long line of phonetic studies that seek to determine and qualify the acoustic cues humans use to identify stop place. The present study draws from a database of 1500 CV tokens of American English and their values for the acoustic features thought to be cues for stop place identification, including (1) VOT, (2) energy of the burst and release, (3) spectrum at the burst, and (4) formant transitions into the following vowel. Decision trees are used to determine the relative invariance of these acoustic features, which indicates their potential to serve as useful cues for listeners cross-contextually. Decision trees thus allow the evaluation of vocalic effects on this hierarchy of features for the purpose of guiding classic perceptual confusion studies.