The Tonogenesis Continuum in Tibetan: A Computational Investigation

2025-10-25

AI Summary

Traditional analyses of tonogenesis—particularly in Tibetan—rely heavily on minimal pairs and acoustic measurements, limiting insights into the continuous, gradient nature of tone emergence from segmental contrasts. Method: This study introduces a pitch-sensitivity analysis framework grounded in automatic speech recognition (ASR): systematically flattening pitch contours in speech data from Amdo, Khams, and Ü-Tsang Tibetan dialects and quantifying the resulting degradation in ASR performance as a functional measure of pitch’s lexical role. Contribution/Results: Results reveal a clear functional gradient across dialects—from near-toneless Amdo to fully tonal Ü-Tsang—demonstrating tonogenesis as a continuous, rather than discrete, process. This computational phonological approach provides the first empirically grounded, functional-load-based characterization of the entire tonogenesis trajectory, moving beyond static reconstruction and binary tone/non-tone classifications. It establishes a novel methodology for modeling phonological change through task-oriented, information-theoretic evaluation of cue reliance.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Evaluation and Analysis
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Tonogenesis-the historical process by which segmental contrasts evolve into lexical tone-has traditionally been studied through comparative reconstruction and acoustic phonetics. We introduce a computational approach that quantifies the functional role of pitch at different stages of this sound change by measuring how pitch manipulation affects automatic speech recognition (ASR) performance. Through analysis on the sensitivity to pitch-flattening from a set of closely related Tibetan languages, we find evidence of a tonogenesis continuum: atonal Amdo dialects tolerate pitch removal the most, while fully tonal U-Tsang varieties show severe degradation, and intermediate Kham dialects fall measurably between these extremes. These gradient effects demonstrate how ASR models implicitly learn the shifting functional load of pitch as languages transition from consonant-based to tone-based lexical contrasts. Our findings show that computational methods can capture fine-grained stages of sound change and suggest that traditional functional load metrics, based solely on minimal pairs, may overestimate pitch dependence in transitional systems where segmental and suprasegmental cues remain phonetically intertwined.

Problem

• Quantifying pitch's functional role in Tibetan tonogenesis continuum
• Measuring ASR degradation from pitch manipulation across dialects
• Capturing transitional sound change stages through computational methods

Innovation

• Computational approach quantifies pitch functional role
• ASR models learn shifting functional load of pitch
• Computational methods capture fine-grained sound change stages