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Exploring tonal contrasts in Dharamshala Tibetan using mathematical modeling, statistical modeling, and machine learning algorithms

ORCiD logo with link | Indian Institute of Technology Ropar
ORCiD logo with link | Indian Institute of Technology Ropar
ORCiD logo with link | Tezpur University | Council for Diversity & Innovation
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Abstract
This paper examines the tonal system of Dharamshala Tibetan (DT) through a comprehensive phonetic and phonological analysis, employing statistical tests, regression modeling, mathematical modeling, and machine learning algorithms (MLAs). Our aim has been to determine the number and nature of tonal contrasts in DT, an issue that remains unresolved to date. The stimuli (tonemes), consisting of two-way and three-way meaning contrasts, are designed based on preliminary fieldwork and consultations with native speakers. Acoustic analyses reveal three distinct tonal categories supported by significant differences in fundamental frequency (f0), including f0 height and f0 directionality, duration, and intensity. Although initial f0 data analysis, particularly f0 directionality, suggested rising contours, further analysis confirmed these as (i) two level tones, viz. high (H) and low (L), and (ii) one contour tone, viz., mid-rising (MR), with consistent pitch trajectories. MLAs were employed to classify the tones, with confusion matrices highlighting the prominence of misclassifications involving mid-rising tones. This supports the hypothesis that the mid-rising tone may be a recently emerged category. The findings of this work are contextualized within the broader framework of Tibetan phonology, providing valuable insights into the tonal characteristics of DT. This study underscores the importance of integrating quantitative approaches with linguistic theory to deepen our understanding of tonal systems.
Article outline
  • 1.Introduction
    • 1.1The status of lexical tone in the Tibetan language
    • 1.2Revised perspectives and current understanding
    • 1.3Research gap and objectives
  • 2.Methodology
    • 2.1Participants
    • 2.2Stimuli
    • 2.3Data recording and annotation
    • 2.4Mathematical modeling to validate f0 directionality
    • 2.5Statistical analysis and model generation
    • 2.6Application of machine learning algorithms
  • 3.Results
    • 3.1Fundamental frequency: f0 directionality
    • 3.2Statistical analysis
    • 3.3Statistical modeling
    • 3.4Mathematical modeling
    • 3.5Application of supervised machine learning algorithms
  • 4.Discussion
  • 5.Conclusions
  • Acknowledgments
  • List of abbreviations
  • References
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