Volume 148, April 2026, 104731Author links open overlay panel, Highlights•

Listeners generalize perceptual learning for biphone phonotactics between talkers.

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Generalization is not increased when multiple talkers evidence a biphone pattern.

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Generalization is stronger between two more-similar talkers.

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Listeners do not track talker-specific biphone distributions.

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Instead, their learning suggests aggregation over talkers.

Abstract

In this study we present a set of experiments testing the conditions under which listeners generalize perceptual learning for biphone phonotactics across talkers. Listeners were exposed to skewed CV biphone distributions at the endpoints of a phonetic VOT continuum with more ambiguous steps having an unbiased (flat) distribution. We first replicated previous findings that listeners generalized learning from biasing contexts to perceive the higher probability biphone at ambiguous continuum steps, when all stimuli were produced by the same talker. Next, we showed that listeners also generalize learning from biphone biasing contexts learned from one talker to a new talker. Notably, there was no further improvement when listeners were presented with multiple talkers producing skewed biphone distributions. Additionally, when exposed to two talkers producing two distinct biphone distributions, listeners failed to learn talker-specific biphone probabilities, and instead evidenced learning for biphone statistics pooled across talkers. That is, listeners extracted talker-independent biphone information. We also found evidence for a possible role for talker similarity: listeners generalized somewhat more strongly between similar talkers, and when acoustic similarity for stimuli within a particular talker is greater. We argue that the results support talker-independent perceptual learning for phonotactics, which is likely learned over representations that are rich enough to encode talker information, separately.

Keywords

Speech perception

Perceptual learning

Phonotactics

Talker adaptation

Data availabilityAll of the stimuli, code for analysis, code for data visualization, and models reported in the paper are stored in an open access repository on the OSF at https://osf.io/9k6r3.

© 2026 The Authors. Published by Elsevier Inc.