TAILIEUCHUNG - Báo cáo khoa học: "Learning Intonation Rules for Concept to Speech Generation"

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. . | Learning Intonation Rules for Concept to Speech Generation Shimei Pan and Kathleen McKeown Dept of Computer Science Columbia University New York NY 10027 USA pan kathy @ Abstract 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 au tomatically summarize patient s pre- during and post- operation status to different .

• Báo cáo khoa học
• Learning Intonation Rules
• Concept to Speech Generation
• Long Papers
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