Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent

TL;DR

This study applies information theory to analyze how prosody encodes lexical distinctions across languages, revealing that tonal languages exhibit higher mutual information between text and pitch, supporting a gradient view of linguistic typology.

Contribution

The paper introduces an information-theoretic approach to quantify prosodic typology, demonstrating that tonal languages have higher mutual information between text and pitch compared to stress-accent languages.

Findings

Tonal languages show higher mutual information between text and pitch.

Pitch curves are more predictable from text in tonal languages.

Entropy of pitch curves is similar across languages.

Abstract

This paper argues that the relationship between lexical identity and prosody -- one well-studied parameter of linguistic variation -- can be characterized using information theory. We predict that languages that use prosody to make lexical distinctions should exhibit a higher mutual information between word identity and prosody, compared to languages that don't. We test this hypothesis in the domain of pitch, which is used to make lexical distinctions in tonal languages, like Cantonese. We use a dataset of speakers reading sentences aloud in ten languages across five language families to estimate the mutual information between the text and their pitch curves. We find that, across languages, pitch curves display similar amounts of entropy. However, these curves are easier to predict given their associated text in the tonal languages, compared to pitch- and stress-accent languages, and thus the mutual information is higher in these languages, supporting our hypothesis. Our results support perspectives that view linguistic typology as gradient, rather than categorical.