19th IEEE International Conference on Tools with Artificial Intelligence - Vol.2 (ICTAI 2007) Segmental Duration Modeling for Greek Speech Synthesis Paris, France October 29-October 31 ISBN: 0-7695-3015-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICTAI.2007.33
In this paper we cope with the task of modeling phoneme duration for Greek speech synthesis. In particular we apply well established machine learning approaches to the WCL-1 prosodic database for predicting segmental durations from shallow morphosyntactic and prosodic features. We employ decision trees, instance based learning and linear regression. Trained on a 5500 word database, both CART and linear regression models proved to be the most effective in terms for the task with a root mean square error of 0.0252 and 0.0251 respectively.
Citation:
Alexandros Lazaridis, Panagiotis Zervas, Georgios Kokkinakis, "Segmental Duration Modeling for Greek Speech Synthesis," ictai, vol. 2, pp.518-521, 19th IEEE International Conference on Tools with Artificial Intelligence - Vol.2 (ICTAI 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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