A Quantitative Model of Yorùbá Speech Intonation Using Stem-ML
We present a quantitative model of Standard Yorùbá (SY) intonation; it is designed to have parameters that are linguistically interpretable. The model is built and trained on speech data from a native speaker of SY. The resulting model reproduces the data well: its Root Mean Square prediction error...
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2007 |
| Country: | Brasil |
| Institution: | Universidade Federal de Lavras (UFLA) |
| Repository: | INFOCOMP: Jornal de Ciência da Computação |
| Language: | English |
| OAI Identifier: | oai:infocomp.dcc.ufla.br:article/185 |
| Online Access: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/185 |
| Access Level: | Open access |
| Keyword: | Intonation modelling Speech synthesis Quantitative model |
| Summary: | We present a quantitative model of Standard Yorùbá (SY) intonation; it is designed to have parameters that are linguistically interpretable. The model is built and trained on speech data from a native speaker of SY. The resulting model reproduces the data well: its Root Mean Square prediction error (RMSE) is 14:00 Hz on a test set. We find that intonation is used to mark sentence and phrase boundaries: beginning syllables are systematically stronger, while ending syllables are systematically weaker than the medial syllables. The M tone is the strongest and the H tone is the weakest, though the differences are modest. We see comparable amounts of carry-over and anticipatory co-articulation. The resulting model for SY shows similar characteristics when compared to Mandarin and Cantonese intonation models. |
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