EYE GAZE FOR UNDERSTANDING CONVERSATIONAL SPEECH

Eye gaze is a useful indication of attention and, as such, can be a valuable feature to improve spoken-language understanding in human-computer interaction. Based on the hypothesis that users look at a link before selecting it, we investigate the use of novel eye-gaze features to improve link click event prediction. Our data comprises users performing a variety of online tasks such as form filling and web browsing, and we show significant performance improvement by incorporating the use of gaze features. In addition, our analysis shows that there is much user-specific variation in gaze, so we are also looking to improve the modeling of gaze by user-and task-specific adaptation.