![]() Together, our analyses provide limited evidence for predictability at different time scales, though higher-order predictability is difficult to reliably infer. Second, we model higher-order temporal structure-regularities arising in an ordered series of syllable timings-testing the hypothesis that non-adjacent temporal structures may explain the gap between subjectively-perceived temporal regularities, and the absence of universally-accepted lower-order objective measures. First, we analyse distributional regularities using two novel techniques: a Bayesian ideal learner analysis, and a simple distributional measure. Rather than looking for differences between languages, we aim to find across languages (using clearly defined acoustic, rather than orthographic, measures), temporal predictability in the speech signal which could be exploited by a language learner. Here, we compare several statistical methods on a sample of 18 languages, testing whether syllable occurrence is predictable over time. ![]() Existing measures of speech timing tend to focus on first-order regularities among adjacent units, and are overly sensitive to idiosyncrasies in the data they describe. This hypothesis tacitly assumes that learners exploit predictability in the temporal structure of speech. By providing on-line clues to the location and duration of upcoming syllables, temporal structure may aid segmentation and clustering of continuous speech into separable units. Temporal regularities in speech, such as interdependencies in the timing of speech events, are thought to scaffold early acquisition of the building blocks in speech. Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium.Yannick Jadoul † Andrea Ravignani † * Bill Thompson † Piera Filippi Bart de Boer
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