Predict the Timing of Backchannels Using Dialog History and Linguistic/Acoustic Features

Abstract

Backchannels could allow spoken dialogue systems to make communication smoother and to elicit more conversation from users. We propose a model that uses acoustic features, linguistic features, and dialogue histories, to predict appropriate timings of backchannels. Our experimental results show that the proposed method performs better than our baseline model that uses acoustic and linguistic features only. Furthermore, we conducted a subjective experiment on predicting timings of backchannels, which results showed that the proposed method can predict the timings of the giving backchannels with a performance similar to that of a human annotator. We obtained a higher evaluation than the baseline model in our five-grade evaluation by seven human subjects, confirming the effectiveness of our proposed method.