Modelled Multivariate Overlap: A method for measuring vowel merger

TL;DR

This paper presents a new multivariate modeling method for measuring vowel merger overlap, improving accuracy and uncertainty estimation over traditional empirical approaches, demonstrated on English dialect data.

Contribution

It introduces a joint modeling approach for acoustic dimensions that enhances vowel overlap measurement and uncertainty quantification.

Findings

Modelled distributions outperform empirical ones in overlap measurement.

Multivariate modeling provides subtle advantages over univariate approaches.

Method effectively evaluates vowel mergers across dialects.

Abstract

This paper introduces a novel method for quantifying vowel overlap. There is a tension in previous work between using multivariate measures, such as those derived from empirical distributions, and the ability to control for unbalanced data and extraneous factors, as is possible when using fitted model parameters. The method presented here resolves this tension by jointly modelling all acoustic dimensions of interest and by simulating distributions from the model to compute a measure of vowel overlap. An additional benefit of this method is that computation of uncertainty becomes straightforward. We evaluate this method on corpus speech data targeting the PIN-PEN merger in four dialects of English and find that using modelled distributions to calculate Bhattacharyya affinity substantially improves results compared to empirical distributions, while the difference between multivariate and univariate modelling is subtle.