Speech dialogue system based on simultaneous understanding

Hatazaki, Kaichiro; Ehsani, Farzad; Noguchi, Jun; Watanabe, Takao

doi:10.1016/0167-6393(94)90082-5

Cited by 3 publications

(2 citation statements)

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“…Some methods use keyword detection (Rose, 1995;Hatazaki et al, 1994;Seto et al, 1994) and key-phrase detection (Aust et al, 1995;Kawahara et al, 1996) to understand speech mainly because the speech recognition score is not high enough.…”

Section: Previous Workmentioning

confidence: 99%

“…In addition, since the algorithms of the probabilistic methods are not designed to work in an incremental way, they cannot be used in real-time analysis in a straightforward way. Some methods use keyword detection (Rose, 1995;Hatazaki et al, 1994;Seto et al, 1994) and key-phrase detection (Aust et al, 1995;Kawahara et al, 1996) to understand speech mainly because the speech recognition score is not high enough. The lack of the full use of syntax in these approaches, however, means user utterances might be misunderstood even if the speech recognition gave the correct answer.…”

Section: Previous Workmentioning

confidence: 99%

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Understanding unsegmented user utterances in real-time spoken dialogue systems

Nakano

Miyazaki

Hirasawa

et al. 1999

Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics -

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This paper proposes a method for incrementally understanding user utterances whose semantic boundaries are not known and responding in real time even before boundaries are determined. It is an integrated parsing and discourse processing method that updates the partial result of understanding word by word, enabling responses based on the partial result. This method incrementally finds plausible sequences of utterances that play crucial roles in the task execution of dialogues, and utilizes beam search to deal with the ambiguity of boundaries as well as syntactic and semantic ambiguities. The results of a preliminary experiment demonstrate that this method understands user utterances better than an understanding method that assumes pauses to be semantic boundaries.

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