Opportunities and Obligations to Take Turns in Collaborative Multi-Party Human-Robot Interaction

In this paper we present a data-driven model for detecting opportunities and obligations for a robot to take turns in multi-party discussions about objects. The data used for the model was collecte ...

[1]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[2]  S. Duncan,et al.  Some Signals and Rules for Taking Speaking Turns in Conversations , 1972 .

[3]  A. Kendon.  Some functions of gaze-direction in social interaction. , 1967, Acta psychologica.

[4]  Gabriel Skantze,et al.  Incremental Dialogue Processing in a Micro-Domain , 2009, EACL.

[5]  Rich Caruana,et al.  Learning speaker, addressee and overlap detection models from multimodal streams , 2012, ICMI '12.

[6]  H. H. Clark.  Coordinating with each other in a material world , 2005 .

[7]  Gabriel Skantze,et al.  Turn-taking, feedback and joint attention in situated human-robot interaction , 2014, Speech Commun..

[8]  Robert Östling,et al.  Stagger: an Open-Source Part of Speech Tagger for Swedish , 2013 .

[9]  David R. Traum,et al.  Embodied agents for multi-party dialogue in immersive virtual worlds , 2002, AAMAS '02.

[10]  Tatsuya Kawahara,et al.  Prediction of Turn-Taking by Combining Prosodic and Eye-Gaze Information in Poster Conversations , 2012, INTERSPEECH.

[11]  Julia Hirschberg,et al.  Turn-taking cues in task-oriented dialogue , 2011, Comput. Speech Lang..

[12]  Gabriel Skantze,et al.  Head Pose Patterns in Multiparty Human-Robot Team-Building Interactions , 2013, ICSR.

[13]  Michael Argyle,et al.  The central Europe experiment: Looking at persons and looking at objects , 1976 .

[14]  Takayuki Kanda,et al.  Conversational gaze mechanisms for humanlike robots , 2012, TIIS.

[15]  Gabriel Skantze,et al.  The furhat Back-Projected humanoid Head-Lip Reading, gaze and Multi-Party Interaction , 2013, Int. J. Humanoid Robotics.

[16]  Jean-Marc Odobez,et al.  Recognizing Visual Focus of Attention From Head Pose in Natural Meetings , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Mattias Heldner,et al.  Pauses, gaps and overlaps in conversations , 2010, J. Phonetics.

[18]  Andreas Stolcke,et al.  Addressee detection for dialog systems using temporal and spectral dimensions of speaking style , 2013, INTERSPEECH.

[19]  Maxine Eskénazi,et al.  Optimizing Endpointing Thresholds using Dialogue Features in a Spoken Dialogue System , 2008, SIGDIAL Workshop.

[20]  Gabriel Skantze,et al.  Data-driven models for timing feedback responses in a Map Task dialogue system , 2014, Comput. Speech Lang..

[21]  Hideki Kawahara,et al.  YIN, a fundamental frequency estimator for speech and music. , 2002, The Journal of the Acoustical Society of America.

[22]  Louis-Philippe Morency,et al.  Predicting Listener Backchannels: A Probabilistic Multimodal Approach , 2008, IVA.

[23]  Tanja Schultz,et al.  Identifying the addressee in human-human-robot interactions based on head pose and speech , 2004, ICMI '04.

[24]  Gabriel Skantze,et al.  IrisTK: a statechart-based toolkit for multi-party face-to-face interaction , 2012, ICMI '12.

[25]  Anton Nijholt,et al.  Eye gaze patterns in conversations: there is more to conversational agents than meets the eyes , 2001, CHI.

[26]  A. Ichikawa,et al.  An Analysis of Turn-Taking and Backchannels Based on Prosodic and Syntactic Features in Japanese Map Task Dialogs , 1998, Language and speech.

[27]  Jie Zhu,et al.  Head orientation and gaze direction in meetings , 2002, CHI Extended Abstracts.

[28]  Eric Horvitz,et al.  Decisions about turns in multiparty conversation: from perception to action , 2011, ICMI '11.