Hierarchical structure and word strength prediction of Mandarin prosody

Greg Kochanski, Chilin Shih, Hongyan Jing

Research output: Contribution to journalArticlepeer-review

Abstract

We use Stem-ML to build an automatic learning system for Mandarin prosody that allows us to make quantitative measurements of prosodic strengths. Stem-ML is a phenomenological model of the muscle dynamics and planning process that controls the tension of the vocal folds. Because Stem-ML describes the interactions between nearby tones or accents, we were able to use a highly constrained model with only one accent template for each lexical tone category, and a single prosodic strength per word. The model accurately reproduces the intonation of the speaker, capturing 87% of the variance of the speech's fundamental frequency, f0. The result reveals strong alternating metrical patterns in words, and suggests that the speaker uses word strength to mark a hierarchy of sentence, clause, phrase, and word boundaries.

Original languageEnglish (US)
Pages (from-to)33-43
Number of pages11
JournalInternational Journal of Speech Technology
Volume6.
Issue number1
DOIs
StatePublished - Jan 2003
Externally publishedYes

Keywords

  • Dynamics
  • Intonation
  • Mandarin Chinese
  • Modeling
  • Prosody
  • Tone

ASJC Scopus subject areas

  • Software
  • Language and Linguistics
  • Human-Computer Interaction
  • Linguistics and Language
  • Computer Vision and Pattern Recognition

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