- 著者
-
Hara Sunao
Kitaoka Norihide
Takeda Kazuya
- 出版者
- ISCA(International Speech Communication Association)
- 雑誌
- 11th Annual Conference of the International Speech Communication Association in Makuhari, Chiba, Japan on September 26-30, 2010 (INTERSPEECH 2010)
- 巻号頁・発行日
- pp.3034-3037, 2010-09-26
In this paper, we propose a method of detecting task- incompleted users for a spoken dialog system using an N-gram- based dialog history model. We collected a large amount of spoken dialog data accompanied by usability evaluation scores by users in real environments. The database was made by a field test in which naive users used a client-server music retrieval system with a spoken dialog interface on their own PCs. An N-gram model was trained from sequences that consist of user dialog acts and/or system dialog acts for two dialog classes, that is, the dialog completed the music retrieval task or the dialog incompleted the task. Then the system detects unknown dialogs that is not completed the task based on the N-gram likelihood. Experiments were conducted on large real data, and the results show that our proposed method achieved good classification performance. When the classifier correctly detected all of the task-incompleted dialogs, our proposed method achieved a false detection rate of 6%.