A Regression-based Approach to Modeling Addressee Backchannels
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Summary
This paper explores how a regression-based approach might offer insights into modeling predictive relationships between speaker behaviors and addressee backch channels in a storytelling scenario and reveals speaker eye contact as a significant predictor of verbal, nonverbal, and bimodal backchannels and utterance boundaries as predictors of nonverbal and bIModalBackchannels.
- Type
- article
- Published
- 2012-07-05
- Cited by
- 19
- References
- 28
- OpenAlex
- https://openalex.org/W56995818
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:8793190
Keywords
Nonverbal communication, Computer science, Utterance, Key (lock), Conversation
References
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- Talking with robots about objects: A system-level evaluation in HRI
- Backchannels across cultures: A study of Americans and Japanese
- Studies in the Way of Words
- Prosodic features which cue back-channel responses in English and Japanese
- To Nod or Not to Nod: An Observational Study of Nonverbal Communication and Status in Female and Male College Students
- Studies in the Way of Words
- Multiple Layers of Meaning in an Oral Proficiency Test: The Complementary Roles of Nonverbal, Paralinguistic, and Verbal Behaviors in Assessment Decisions
- Stance, Alignment, and Affiliation During Storytelling: When Nodding Is a Token of Affiliation
Cited by
- PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling
- Multivariate evaluation of interactive robot systems
- Designing a Motivational Agent for Behavior Change in Physical Activity
- Reinforcement Learning of Cooperative Persuasive Dialogue Policies using Framing
- Learning cooperative persuasive dialogue policies using framing
- Apprenticeship Scheduling: Learning to Schedule from Human Experts
- Unimodal and Bimodal Backchannels in Conversational English
- Human-Machine Collaborative Optimization via Apprenticeship Scheduling
- Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration
- Personalized Apprenticeship Learning from Heterogeneous Decision-Makers
- Analyzing Multifunctionality of Head Movements in Face-to-Face Conversations Using Deep Convolutional Neural Networks
- Deep Transfer Learning for Recognizing Functional Interactions via Head Movements in Multiparty Conversations
- MultiMediate'22: Backchannel Detection and Agreement Estimation in Group Interactions
- Interdisciplinary Corpus-based Approach for Exploring Multimodal Conversational Feedback
- The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective
- Backchannel Detection and Agreement Estimation from Video with Transformer Networks
- A multimodal model for predicting feedback position and type during conversation
- The Distracted Ear: How Listeners Shape Conversational Dynamics
- Construction and Analysis of a Persuasive Dialogue Corpus
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