Multimodal Voice Activity Projection for Turn-Taking in Social Robots with Voice-Activity-Related Pretrained Encoders
arXiv:2607.07294v1 Announce Type: cross Abstract: Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot must anticipate conversational dynamics rather than merely react to pauses. This work presents a Multimodal Voice Activity Projection (MM-VAP) framework that extends the original audio-only VAP formulation to synchronized audio-visual inputs while preserving its self-supervised future-projection objective. The proposed approach builds on pretrained audio-visual backbones originally optimized for s
The increasing sophistication of AI models and the demand for more natural human-robot interaction are driving advancements in multimodal perception for social robots.
This development moves social robots beyond reactive responses towards more proactive and context-aware participation in complex human conversations, enhancing their utility in mediator roles.
Social robots will be able to predict conversational turns more effectively, leading to smoother and more intuitive interactions that significantly improve user experience and robotic functionality.
- · Social robotics manufacturers
- · AI developers specializing in multimodal AI
- · Service industries deploying social robots
- · Consumers interacting with robots
- · Developers of reactive-only social robots
- · Companies relying on simpler, audio-only interaction models
More natural and efficient human-robot communication will become a standard expectation.
The improved interactive capabilities will accelerate the deployment of social robots in various public-facing and collaborative environments.
Ethical frameworks for human-robot interaction will need to evolve rapidly to address sophisticated AI conversational agents and their influence.
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Read at arXiv cs.CL