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 exploit predictability in 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 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. 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.

Rhythm in speech and animal vocalizations: a cross‐species perspective

“…The speech dataset comes from “The north wind and sun dataset” corpus, consisting of recordings of the fable in 18 different languages. For our analysis, we use the position of the syllable centers annotated by Jadoul and colleagues . This annotated speech dataset contains the syllable centers of all 18 languages.…”

“…For our analysis, we use the position of the syllable centers annotated by Jadoul and colleagues. 158 This annotated speech dataset contains the syllable centers of all 18 languages.…”

Dynamics of functional networks for syllable and word-level processing

“…Emerging evidence from neurophysiological studies points to interactions of higher level speech processing and syllable processing. For example, predictions arising from multiple processing levels such as phonological or syntactic/semantic processing (22)(23)(24)(25)(26) might inform lower levels. The top-down modulation of the phase-alignment of low-frequency auditory cortical oscillations to speech acoustics is suggested by increased speech-tracking for intelligible versus unintelligible speech (27,28,12,29), which was accompanied by increased connectivity between auditory cortex and higher-level processing areas, such as frontal and motor cortex (29,30).…”

Evolution of speech rhythm: a cross-species perspective

“…Additionally, the isochronous and the hierarchical series are jittered with Gaussian noise with a standard deviation of 0.005 s. The speech dataset comes from "The north wind and sun dataset" corpus, consisting of recordings of the fable in 18 different languages. For our analysis, we use the position of the syllable centers annotated by 84 . This annotated speech dataset contains the syllable centers of all languages.…”