# Does the Appearance of Autonomous Conversational Robots Affect User Spoken Behaviors in Real-World Conference Interactions?

arXiv: https://arxiv.org/abs/2503.13625 · HTML: https://mlsanity.com/paper/2503.13625/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 64 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [Responsive Listening Head Generation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2112.13548/) (2021)
- [TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages](https://mlsanity.com/paper/2211.16496/) (2022)
- [Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality](https://mlsanity.com/paper/2110.04614/) (2021)
- [Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension](https://mlsanity.com/paper/2012.07410/) (2020)
- [A Framework for Integrating Gesture Generation Models into Interactive Conversational Agents](https://mlsanity.com/paper/2102.12302/) (2021)
- [Acknowledgment of Emotional States: Generating Validating Responses for Empathetic Dialogue](https://mlsanity.com/paper/2402.12770/) (2024)
- [Human-Like Embodied AI Interviewer: Employing Android ERICA in Real International Conference](https://mlsanity.com/paper/2412.09867/) (2024)
- [Social Robots As Companions for Lonely Hearts: The Role of Anthropomorphism and Robot Appearance](https://mlsanity.com/paper/2306.02694/) (2023)
- [Analysis and Detection of Differences in Spoken User Behaviors between Autonomous and Wizard-of-Oz Systems](https://mlsanity.com/paper/2410.03147/) (2024)

## Built on by (most cited first, 1 of 1)

- [Closing the Affective Loop: Multimodal Speaker-Listener Emotion-Dynamics-Aware Empathetic Social Robots](https://mlsanity.com/paper/2608.16686/) (2026)

## Abstract (arXiv)

We investigate the impact of robot appearance on users' spoken behavior during real-world interactions by comparing a human-like android, ERICA, with a less anthropomorphic humanoid, TELECO. Analyzing data from 42 participants at SIGDIAL 2024, we extracted linguistic features such as disfluencies and syntactic complexity from conversation transcripts. The results showed moderate effect sizes, suggesting that participants produced fewer disfluencies and employed more complex syntax when interacting with ERICA. Further analysis involving training classification models like Na"ive Bayes, which achieved an F1-score of 71.60%, and conducting feature importance analysis, highlighted the significant role of disfluencies and syntactic complexity in interactions with robots of varying human-like appearances. Discussing these findings within the frameworks of cognitive load and Communication Accommodation Theory, we conclude that designing robots to elicit more structured and fluent user speech can enhance their communicative alignment with humans.