  Metric functions for phoneme perception capture the similarity structure
among phonemes in a given language and therefore play a central role in
phonology and psycho-linguistics. Various phenomena depend on phoneme
similarity, such as spoken word recognition or serial recall from verbal
working memory. This study presents a new framework for learning a metric
function for perceptual distances among pairs of phonemes. Previous studies
have proposed various metric functions, from simple measures counting the
number of phonetic dimensions that two phonemes share (place-,
manner-of-articulation and voicing), to more sophisticated ones such as
deriving perceptual distances based on the number of natural classes that both
phonemes belong to. However, previous studies have manually constructed the
metric function, which may lead to unsatisfactory account of the empirical
data. This study presents a framework to derive the metric function from
behavioral data on phoneme perception using learning algorithms. We first show
that this approach outperforms previous metrics suggested in the literature in
predicting perceptual distances among phoneme pairs. We then study several
metric functions derived by the learning algorithms and show how perceptual
saliencies of phonological features can be derived from them. For English, we
show that the derived perceptual saliencies are in accordance with a previously
described order among phonological features and show how the framework extends
the results to more features. Finally, we explore how the metric function and
perceptual saliencies of phonological features may vary across languages. To
this end, we compare results based on two English datasets and a new dataset
that we have collected for Hebrew.

Machine Learning;Sound;Audio and Speech Processing;Machine Learning