Abstract
In this paper, we introduce a practical spoken dialogue interface for intelligent TV based on goal-oriented dialogue modeling. It uses a frame structure for representing the user intention and determining the next action. To analyze discourse context, we employ several statistical learning techniques and device an incremental dialogue strategy learning method from training corpus. By empirical experiments, we demonstrated the efficiency of the proposed system. In case of the subjective evaluation, we obtained 73% user satisfaction ratio, while the objective evaluation result was over 90% in case of a restricted situation for commercialization1. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Oh, H. J., Lee, C. H., Hwang, Y. G., & Jang, M. G. (2007). Dialogue management for intelligent TV based on statistical learning method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4629 LNAI, pp. 644–652). Springer Verlag. https://doi.org/10.1007/978-3-540-74628-7_83
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