Prediction of Korean Prosodic Phrase Boundary by Efficient Feature Selection in Machine Learning

Prediction of the prosodic phrase boundary is a potent influence on the performance of speech recognition and voice synthesis systems. We propose a statistical approach using efficient learning features for the natural prediction of the Korean prosodic phrase boundary. These new features reflect factors that affect the generation of the prosodic phrase boundary better than existing learning features. Notably, moreover, learning features that are extracted according to the hand-crafted prosodic phrase prediction rule impart higher accuracy. We evaluated the new learning features in terms of their efficiency in predicting the prosodic phrase boundary, using CRFs (conditional random fields). The results were 84.63% accuracy for three levels and 80.14% accuracy for six levels.