title: Seeking Temporal Predictability in Speech: Comparing Statistical Approaches on 18 World Languages.
abstract: 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. 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. This hypothesis tacitly assumes that learners exploitpredictabilityin the temporal structure of speech. 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. Here, we compare several statistical methods on a sample of 18 languages, testing whether syllable occurrence is predictable over time. 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. First, we analyse distributional regularities using two novel techniques: a Bayesian ideal learner analysis, and a simple distributional measure. Second, we modelhigher-ordertemporal structure-regularities arising in an orderedseriesof 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. Together, our analyses provide limited evidence for predictability at different time scales, though higher-order predictability is difficult to reliably infer. We conclude that temporal predictability in speech may well arise from a combination of individually weak perceptual cues at multiple structural levels, but is challenging to pinpoint.
authors.affiliation: Artificial Intelligence Lab, Vrije Universiteit Brussel Brussels, Belgium.
authors.forename: Yannick
authors.initials: Y
authors.lastname: Jadoul
authors.affiliation: Artificial Intelligence Lab, Vrije Universiteit Brussel Brussels, Belgium.
authors.forename: Andrea
authors.initials: A
authors.lastname: Ravignani
authors.affiliation: Artificial Intelligence Lab, Vrije Universiteit Brussel Brussels, Belgium.
authors.forename: Bill
authors.initials: B
authors.lastname: Thompson
authors.affiliation: Artificial Intelligence Lab, Vrije Universiteit Brussel Brussels, Belgium.
authors.forename: Piera
authors.initials: P
authors.lastname: Filippi
authors.affiliation: Artificial Intelligence Lab, Vrije Universiteit Brussel Brussels, Belgium.
authors.forename: Bart
authors.initials: B
authors.lastname: de Boer
keywords: Bayesian; autoregressive models; nPVI; rhythm; speech perception; temporal structure; time series; timing