@article{jcsa2013142,
author={Zervas, Panagiotis},
title={Prosodic Boundary Prediction for Greek Speech Synthesis},
journal={Journal of Computer Sciences and Applications},
volume={1},
number={4},
pages={61--74},
year={2013},
url={http://pubs.sciepub.com/jcsa/1/4/2},
issn={2328-725X},
abstract={In this article, we evaluate features and algorithms for the task of prosodic boundary prediction for Greek. For this purpose a prosodic corpus composed of generic domain text was constructed. Feature contribution was evaluated and ranked with the application of information gain ranking and correlation-based feature selection filtering methods. Resulted datasets were applied to C4.5 decision tree, one-neighbour instance based learner and Bayesian learning methods. Models performance exploitation led as to the construction of a practically optimal feature set whose prediction effectiveness was evaluated with two prosodic databases. In terms of total accuracy and F-measure, evaluation results established the decision tree effectiveness in learning rules for prosodic boundary prediction.},
doi={10.12691/jcsa-1-4-2}
publisher={Science and Education Publishing}
}