This paper describes the incorporation of contextual information into spoken dialogue systems in the database search task. Appropriate dialogue modeling is required to manage automatic speech recognition (ASR) errors using dialogue-level information. We define two dialogue models: a model for dialogue flow and a model of structured dialogue history. The model for dialogue flow assumes dialogues in the database search task consist of only two modes. In the structured dialogue history model, query conditions are maintained as a tree structure, taking into consideration their inputted order. The constraints derived from these models are integrated by using a decision tree learning, so that the system can determine a dialogue act of the utterance and whether each content word should be accepted or rejected, even when it contains ASR errors. The experimental result showed that our method could interpret content words better than conventional one without the contextual information. Furthermore, it was also shown that our method was domain-independent because it achieved equivalent accuracy in another domain with-out any more training.
|Number of pages||4|
|Publication status||Published - 2005 Dec 1|
|Event||9th European Conference on Speech Communication and Technology - Lisbon, Portugal|
Duration: 2005 Sep 4 → 2005 Sep 8
|Conference||9th European Conference on Speech Communication and Technology|
|Period||05/9/4 → 05/9/8|
ASJC Scopus subject areas