Abstract
We propose a method of detecting "task incomplete" dialogs in spoken dialog systems using N-gram-based dialog models. We used a database created during a field test in which inexperienced users used a client-server music retrieval system with a spoken dialog interface on their own PCs. In this study, the dialog for a music retrieval task consisted of a sequence of user and system tags that related their utterances and behaviors. The dialogs were manually classified into two classes: the dialog either completed the music retrieval task or it didn't. We then detected dialogs that did not complete the task, using N-gram probability models or a Support Vector Machine with N-gram feature vectors trained using manually classified dialogs. Offline and on-line detection experiments were conducted on a large amount of real data, and the results show that our proposed method achieved good classification performance.
Original language | English |
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Pages (from-to) | 1305-1308 |
Number of pages | 4 |
Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
Publication status | Published - Dec 1 2011 |
Externally published | Yes |
Event | 12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy Duration: Aug 27 2011 → Aug 31 2011 |
Keywords
- Breakdowns in dialog
- N-gram
- Spoken dialog system
- Task incomplete dialog detection
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modelling and Simulation