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. Through 5-fold cross-validation, we show that the learned rules achieve around 90% accuracy for break index, boundary tone and phrase accent and 80% accuracy for pitch accent. Our study is unique in its use of features produced by language generation to control intonation. The methodology adopted here can be employed directly when more discourse/pragmatic information is to be considered in the future. 1 Motivation Speech is rapidly becoming a viable medium for interaction with real-world applications. Spoken language interfaces to on-line information, such as plane or train schedules, through display-less systems, such as telephone interfaces, are well under development. Speech interfaces are also widely used in applications where eyes-free and hands-free communication is critical, such as car navigation. Natural language generation (NLG) can enhance the ability of such systems to communicate naturally and effectively by allowing the system to tailor, reorganize, or summarize lengthy database responses. For example, in our work on a multimedia generation system where speech and graphics generation techniques are used to automaticaily summarize patient's pre-, during, and post-, operation status to different caregivers (Dalai et al., 1996), records relevant to patient status can easily number in the thousands. Through content planning, sentence planning and lexical selection, ,the NLG component is able to provide a concise, yet informative, briefing automatically through spoken and written language coordinated with graphics . Integrating language generation with speech synthesis within a Concept-to-Speech (CTS) system not only brings the individual benefits of each; as an integrated system, CTS can take advantage of the availability of rich structural information constructed by the underlying NLG component to improve the quality of synthesized speech. Together, they have the potential of generating better speech than Text-to-Speech (TTS) systems. In this paper, we present a series of experiments that use machine learning to identify correlation between intonation and features produced by a robust language generation tool, the FUF/SURGE system (Elhadad, 1993; Robin, 1994) . The ultimate goal of this study is to provide a spoken language generation tool based on FUF/SURGE, extended with an intonation generation component to facilitate the development of new CTS applications.
Other comprehensive pitch accent models have been suggested in REF in the framework of Concept-to-Speech generation where the output of a natural language generation system is used to predict pitch accent.
Learning Intonation Rules for Concept to Speech Generation
COLING-ACL
Computer Science