Predicting Listener Backchannels: A Probabilistic Multimodal Approach

@inproceedings{morency:dekok:gratch:iva:2008, 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-to-human 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.}, author = {Morency, Louis-Philippe and de Kok, Iwan and Gratch, Jonathan }, booktitle = {Intelligent Virtual Agents (IVA '08)}, citeulike-article-id = {3374506}, doi = {http://dx.doi.org/10.1007/978-3-540-85483-8\_18}, journal = {Intelligent Virtual Agents}, keywords = {eye, gaze, speech, turntaking}, pages = {176--190}, posted-at = {2008-10-04 19:40:05}, priority = {2}, title = {Predicting Listener Backchannels: A Probabilistic Multimodal Approach}, url = {http://dx.doi.org/10.1007/978-3-540-85483-8\_18}, year = {2008} }

TY - CONF ID - morency:dekok:gratch:iva:2008 L3 - citeulike-article-id:3374506 N2 - 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-to-human 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. JF - Intelligent Virtual Agents EP - 190 TI - Predicting Listener Backchannels: A Probabilistic Multimodal Approach T2 - Intelligent Virtual Agents (IVA '08) SP - 176 KW - eye KW - gaze KW - speech KW - turntaking AU - Morency, Louis-Philippe AU - de Kok, Iwan AU - Gratch, Jonathan PY - 2008/// UR - http://dx.doi.org/10.1007/978-3-540-85483-8_18 ER -