In this paper, we report on an effort to provide a general-purpose spoken language generation tool for Concept-to-Speech (CTS) applications by extending a widely used text generation package, FUF/SURGE, with an intonation generation component. As a first step, we applied machine learning and statistical models to learn intonation rules based on the semantic and syntactic information typically represented in FUF/SURGE at the sentence level. The results of this study are a set of intonation rules learned automatically which can be directly implemented in our intonation generation component. .
