extended-abstract

An integrated model for predicting backchannel feedbacks

Authors:

Philippe Blache,

Massina Abderrahmane,

Stéphane Rauzy,

Roxane BertrandAuthors Info & Claims

IVA '20: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

Article No.: 6, Pages 1 - 3

https://doi.org/10.1145/3383652.3423948

Published: 19 October 2020 Publication History

Abstract

We present in this paper a method for generating in real time a great variability of multimodal backchannel feedbacks, increasing the naturalness of IVAs. The originality of the approach lies in its capacity to generate all types of features into a unique loop.

References

[1]

J. Bavelas, L. Cates, and T.Johnson. 2000. Listeners as co-narrators. Journal of Personality and Social Psychology 79, 6 (2000).

[2]

R. Bertrand and R. Espesser. 2017. Co-narration in French conversation storytelling: A quantitative insight. Journal of Pragmatics 111 (2017).

[3]

E. Bevacqua, S. Pammi, S. J. Hyniewska, M. Schröder, and C. Pélachaud. 2010. Multimodal Backchannels for Embodied Conversational Agents. In IVA-2010.

[4]

P. Blache. 2017. Dialogue management in task-oriented dialogue systems. In International Workshop on Investigating Social Interactions with Artificial Agents.

Digital Library

[5]

Z. Chen, R. Yang, Z. Zhao, D. Cai, and X. He. 2018. Dialogue Act Recognition via CRF-Attentive Structured Network. In SIGIR.

[6]

A. Stolcke et al. 2000. Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational linguistics 26, 3 (2000).

[7]

G. Ferré and S. Renaudier. 2017. Unimodal and Bimodal Backchannels in Conversational English. In Workshop on the Semantics and Pragmatics of Dialogue.

[8]

A. Gravano and J. Hirschberg. 2011. Turn-taking cues in task-oriented dialogue. Computer Speech and Language 25, 3 (2011).

[9]

S. Kopp, H. van Welbergen, R. Yaghoubzadeh, and H. Buschmeier. 2014. An Architecture for Fluid Real-time Conversational Agents: Integrating Incremental Output Generation and Input Processing. JMUI 8 (2014).

[10]

R. Meena, G. Skantze, and J. Gustafson. 2014. Data-driven models for timing feedback responses in a Map Task dialogue system. Computer Speech and Language 28, 4 (2014).

[11]

M. Ochs, D. Mestre, G. Montcheuil, J.-M. Pergandi, J. Saubesty, E. Lombardo, D. Francon, and P. Blache. 2018. Training doctors' social skills to break bad news: Evaluation of the impact of virtual environment displays on the sense of presence. Journal on Multimodal User Interfaces (2018).

[12]

B. Penteado, M. Ochs, R. Bertrand, and P. Blache. 2019. Evaluating Temporal Predictive Features for Virtual Patients Feedbacks. In IVA-2019.

[13]

R. Poppe, K. P. Truong, D. Reidsma, and D. Heylen. 2010. Backchannel strategies for artificial listeners. In IVA-2010.

[14]

C. Porhet, M. Ochs, J. Saubesty, G. de Montcheuil, and R. Bertrand. 2017. Mining a Multimodal Corpus of Doctor's Training for Virtual Patient's Feedbacks. In International Conference on Multimodal Interaction (ICMI).

[15]

J. Tolins and J. Fox Tree. 2014. Addressee backchannels steer narrative development. Journal of Pragmatics 70 (2014).

[16]

N. Ward and W. Tsukahara. 2000. Prosodic features which cue back-channel responses in English and Japanese. Journal of Pragmatics 32, 8 (2000).

Cited By

Dolgikh ASamsonovich ATikhomirova D(2024)Social–Emotional Conversational Agents Based on Cognitive Architectures and Machine LearningPattern Recognition and Image Analysis10.1134/S105466182470064034:3(765-772)Online publication date: 17-Oct-2024
https://doi.org/10.1134/S1054661824700640
Dolgikh ASamsonovich A(2024)A Socially Acceptable Conversational Agent Based on Cognitive Modeling and Machine LearningBiologically Inspired Cognitive Architectures 202310.1007/978-3-031-50381-8_31(312-322)Online publication date: 14-Feb-2024
https://doi.org/10.1007/978-3-031-50381-8_31
Amer ABhuvaneshwara CAddluri GShaik MBonde VMüller P(2023)Backchannel Detection and Agreement Estimation from Video with Transformer Networks2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191640(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191640
Show More Cited By

Index Terms

An integrated model for predicting backchannel feedbacks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models

Recommendations

The ALICO corpus: analysing the active listener

The Active Listening Corpus (ALICO) is a multimodal data set of spontaneous dyadic conversations in German with diverse speech and gestural annotations of both dialogue partners. The annotations consist of short feedback expression transcriptions with ...
Influence of personality traits on backchannel selection
IVA'10: Proceedings of the 10th international conference on Intelligent virtual agents

Our aim is to build a real-time Embodied Conversational Agent able to act as an interlocutor in interaction, generating automatically verbal and non verbal signals. These signals, called backchannels, provide information about the listener's mental ...
The Intonation of Backchannel Tokens in Italian Collaborative Dialogues
Human Language Technology Challenges for Computer Science and Linguistics
Abstract
This paper offers a contribution to the intonational modelling of backchannel lexical and non-lexical tokens in Italian, which can be used for improving naturalness in voice-based dialogue systems. Results of pragmatic and intonation analysis of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IVA '20: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

October 2020

394 pages

ISBN:9781450375863

DOI:10.1145/3383652

Copyright © 2020 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2020

Check for updates

Author Tags

Qualifiers

Extended-abstract
Research
Refereed limited

Funding Sources

ILCB

Conference

IVA '20

Sponsor:

SIGAI

IVA '20: ACM International Conference on Intelligent Virtual Agents

October 20 - 22, 2020

Scotland, Virtual Event, UK

Acceptance Rates

Overall Acceptance Rate 53 of 196 submissions, 27%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
206
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)2

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Dolgikh ASamsonovich ATikhomirova D(2024)Social–Emotional Conversational Agents Based on Cognitive Architectures and Machine LearningPattern Recognition and Image Analysis10.1134/S105466182470064034:3(765-772)Online publication date: 17-Oct-2024
https://doi.org/10.1134/S1054661824700640
Dolgikh ASamsonovich A(2024)A Socially Acceptable Conversational Agent Based on Cognitive Modeling and Machine LearningBiologically Inspired Cognitive Architectures 202310.1007/978-3-031-50381-8_31(312-322)Online publication date: 14-Feb-2024
https://doi.org/10.1007/978-3-031-50381-8_31
Amer ABhuvaneshwara CAddluri GShaik MBonde VMüller P(2023)Backchannel Detection and Agreement Estimation from Video with Transformer Networks2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191640(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191640
Amoyal MBertrand RBigi BBoudin AMeunier CPallaud BPriego-Valverde BRauzy STellier M(2022)Principes et outils pour l’annotation des corpusPrinciples and tools for corpus annotationTIPA. Travaux interdisciplinaires sur la parole et le langage10.4000/tipa.5424Online publication date: 31-Dec-2022
https://doi.org/10.4000/tipa.5424
Müller PDietz MSchiller DThomas DLindsay HGebhard PAndré EBulling AMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)MultiMediate'22Proceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3551589(7109-7114)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3551589
Ishii RRen XMuszynski MMorency L(2021)Multimodal and Multitask Approach to Listener's Backchannel PredictionProceedings of the 21st ACM International Conference on Intelligent Virtual Agents10.1145/3472306.3478360(131-138)Online publication date: 14-Sep-2021
https://dl.acm.org/doi/10.1145/3472306.3478360
Boudin ABertrand RRauzy SOchs MBlache P(2021)A Multimodal Model for Predicting Conversational FeedbacksText, Speech, and Dialogue10.1007/978-3-030-83527-9_46(537-549)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-83527-9_46

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents