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.

本文介绍了一种新的方法来量化元音重叠。通过同时建模所有感兴趣的声学维度，并通过从模型中模拟分布来计算元音重叠程度，解决了先前使用多元测量和控制不平衡数据和外部因素之间的紧张关系的问题。使用模型化的分布来计算巴氏距亲显著提高了结果，而多元和单元建模之间的差异微妙。