Categorial-grammar-based phrase break prediction

Part-of-speech (POS) has been widely used as the main feature for predicting phrase breaks in text-to-speech synthesis (TTS) systems. However, POS does not clearly represent syntactic information that is necessary for analyzing the grammatical tree structure of a language to assign phrase breaks. Instead of using POS, this paper proposes to use categorial grammar (CG), which embeds fine syntactic information, for Thai as a key feature to predict phrase breaks in Thai Texts. The performances of phrase break predictions using CG, POS, and their reduced sets are compared using classification and regression tree (CART) for learning and predicting phrase break locations. The experimental results showed that the phrase break prediction using CGs as the main feature gave the best performance among the tested features (Precision=73.15%, Recall = 96.96%, F-measure=83.39%).