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 language | English (US) |
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Pages (from-to) | 33-43 |
Number of pages | 11 |
Journal | International Journal of Speech Technology |
Volume | 6. |
Issue number | 1 |
DOIs | |
State | Published - Jan 2003 |
Externally published | Yes |
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