Pitch Accent Prediction Usin Learnin

In this study, we applied ensemble machine learning to predict pitch accents. With decision tree as the baseline algorithm, two popular ensemble learning methods, bagging and boosting, were evaluated across different experiment conditions: using acoustic features only, using text-based features only; using both acoustic and text-based features. F0 related acoustic features are derived from underlying pitch targets. Models of four ToBI pitch accent types (High, Down-stepped high, Low, and Unaccented) are built at the syllable level. Results showed that in all experiments improved performance was achieved by ensemble learning. The best result was obtained in the third task, in which the overall correct rate increases from 84.26% to 87.17%.