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Robot-moderated group discussions have the potential to facilitate engaging and productive interactions among human participants. Previous work on topic management in conversational agents has predominantly focused on human engagement and topic personalization, with the agent having an active role in the discussion. Also, studies have shown the usefulness of including robots in groups, yet further exploration is still needed for robots to learn when to change the topic while facilitating discussions. Accordingly, our work investigates the suitability of machine-learning models and audiovisual non-verbal features in predicting appropriate topic changes. We utilized interactions between a robot moderator and human participants, which we annotated and used for extracting acoustic and body language-related features. We provide a detailed analysis of the performance of machine learning approaches using sequential and non-sequential data with different sets of features. The results indicate promising performance in classifying inappropriate topic changes, outperforming rule-based approaches. Additionally, acoustic features exhibited comparable performance and robustness compared to the complete set of multimodal features. Our annotated data is publicly available at https://github.com/ghadj/topic-change-robot-discussions-data-2024.

IEEE RO-MAN 2024

Let&#39;s Move On: Topic Change in Robot-Facilitated Group Discussions

machine learning and adaptation; multimodal interaction and conversational skills; social intelligence for robots

Let's Move On: Topic Change in Robot-Facilitated Group Discussions

technical paper

[![](https://assets.underline.io/markdown_image/1/image/f25361a85b7c83a7476c0b3c1c0bb1b2.png)](https://drive.google.com/file/d/18jcAcciCBQHj6R07Dbpm2HCOBEIwAOCU/view?usp=sharing)


Dear Colleagues and Friends,

The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, was held at Pasadena Convention Center in Pasadena, USA.

The theme of the IEEE RO-MAN 2024 was **“Embracing Human-Centered HRI”**.

With the continuous technological effort of understanding human needs, creating human-desired robotic systems, and developing real human user experiences, we are marching together towards building a promising and excellent human quality of life, all for our wellness and our next generation.

The conference provided a forum for researchers and practitioners to discuss a variety of topics related to the broad theme of Robot and Human Interactive Communication involving theories, methodologies, technologies, empirical and experimental studies, and generated more insights for the future of Robot and Human Interactive Communication!

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The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

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This paper investigates the effects of gender neutralization on the perception of anthropomorphism, gender specificity, and novelty for human voices, comparing spoken and hummed voice modalities. We evaluated gender-neutralized and original voice samples in both spoken and hummed formats using an online survey. Our results confirm that gender-neutralizing filters effectively reduce perceived gender specificity in both modalities, supporting their use in creating gender-neutral voices for humanoid robots. Hummed voices were perceived as more anthropomorphic and less novel than spoken voices, suggesting that non-verbal sound modalities can enhance the human likeness of gender-neutral androids while maintaining gender ambiguity. The study contributes to HRI by highlighting the potential of humming to fulfill users' expectations of interaction with android robots.

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Let's Move On: Topic Change in Robot-Facilitated Group Discussions

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