Segmental Duration Modeling for Greek Speech Synthesis

Authors:
Alexandros Lazaridis;Panagiotis Zervas;Georgios Kokkinakis
Affiliations:
-;-;-
Venue:
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Year:
2007

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Abstract

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.