Abstract
Cantonese is a major Chinese dialect with a complicated tone system. This research focuses on quantitative modeling of Cantonese tones. It uses Stem-ML, a language-independent framework for quantitative intonation modeling and generation. A set of F0 prediction models are built, and trained on acoustic data. The prediction error is about 11 Hz or 1 semitone. The resulting optimal model parameters are analyzed in accordance with linguistic knowledge. Key observations include: (1) There is no obvious advantage to model the entering tones separately. They can be considered as simply truncated versions of the nonentering tones; (2) Cantonese appears to have a declining phrase intonation; (3) Tones at initial positions of a phrase or a sentence tend to have a greater prosodic strength than those at the final positions; (4) Content words are stronger than function words; (5) Long words are stronger than short words.
Original language | English (US) |
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Title of host publication | 7th International Conference on Spoken Language Processing, ICSLP 2002 |
Publisher | International Speech Communication Association |
Pages | 2401-2404 |
Number of pages | 4 |
State | Published - 2002 |
Externally published | Yes |
Event | 7th International Conference on Spoken Language Processing, ICSLP 2002 - Denver, United States Duration: Sep 16 2002 → Sep 20 2002 |
Other
Other | 7th International Conference on Spoken Language Processing, ICSLP 2002 |
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Country/Territory | United States |
City | Denver |
Period | 9/16/02 → 9/20/02 |
ASJC Scopus subject areas
- Language and Linguistics
- Linguistics and Language