Abstract. During face-to-face interactions, listeners use backchannel feedback such as head nods as a signal to the speaker that the communication is working and that they should continue speaking. Predicting these backchannel opportunities is an important milestone for building engaging and natural virtual humans. In this paper we show how sequential probabilistic models (e.g., Hidden Markov Model or Conditional Random Fields) can automatically learn from a database of human-tohuman interactions to predict listener backchannels using the speaker multimodal output features (e.g., prosody, spoken words and eye gaze). The main challenges addressed in this paper are automatic selection of the relevant features and optimal feature representation for probabilistic models. For prediction of visual backchannel cues (i.e., head nods), our prediction model shows a statistically significant improvement over a previously published approach based on hand-crafted rules.
In REF , sequential probabilistic models were used to select multimodal features from a speaker (e.g. prosody, gaze and spoken words) to predict visual back-channel cues (e.g. head nods).
Predicting listener backchannels: A probabilistic multimodal approach
In In proceedings of Intelligent Virtual Agents (IVA
Computer Science