Addressee Identification In Face-to-Face Meetings
Year: 2006
Type: conference-paper
Abstract: We present results on addressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance and conversational context features. Then, we explore whether information about meeting context can aid classifiers’ performances. Both classifiers perform the best when conversational context and utterance features are combined with speaker’s gaze information. The classifiers show little gain from information about meeting context. 1
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1554401341