In conversational dialogue systems, users prefer to speak at any time and to use natural expressions. We have developed an Independent Component Analysis (ICA) based semi-blind source separation method, which allows users to barge-in over system utterances at any time. We created a novel method from timing information derived from barge-in utterances to identify one item that a user indicates during system enumeration. First, we determine the timing distribution of user utterances containing referential expressions and then approximate it using a gamma distribution. Second, we represent both the utterance timing and automatic speech recognition (ASR) results as probabilities of the desired selection from the system's enumeration. We then integrate these two probabilities to identify the item having the maximum likelihood of selection. Experimental results using 400 utterances indicated that our method outperformed two methods used as a baseline (one of ASR results only and one of utterance timing only) in identification accuracy.
|ジャーナル||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|出版ステータス||Published - 2009 11 26|
|イベント||10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom|
継続期間: 2009 9 6 → 2009 9 10
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