eng Science and Education Publishing Journal of Computer Sciences and Applications 2328-725X 2013-05-19 1 1 61 74 10.12691/jcsa-1-4-2 JCSA2013142 article Panagiotis Zervas pzervas@staff.teicrete.gr 1 Department of Music Technology & Acoustics, Technological Educational Institute of Crete, Rethymnon Branch, Greece 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. http://pubs.sciepub.com/jcsa/1/4/2/jcsa-1-4-2.pdf prosody phrase breaks ToBI C45 IB1 bayesian learning