Formant detail needed for vowel identification

Abstract

Changes in formant frequency over time are important for vowel identification: listeners identify stimuli containing time-varying formants better than stimuli with steady-state formants. Statistically based pattern classifiers used as models for human perception have shown that very coarse representations of formant change over time result in accurate classification of American English vowels. In this study, using synthetic stimuli with five levels of formant contour detail, human listeners achieved maximum vowel identification for relatively coarse representations of formant movement containing information about onset, offset, and midpoint frequencies. More detailed representations of contour did not improve identification for most vowels.