Voice Activity Projection Model with Multimodal Encoders

• URL: http://arxiv.org/abs/2506.03980v1
• Date: Wed, 04 Jun 2025 14:10:03 GMT
• Title: Voice Activity Projection Model with Multimodal Encoders
• Authors: Takeshi Saga, Catherine Pelachaud,
• Abstract summary: We propose a multimodal model enhanced with pre-trained audio and face encoders to improve performance by capturing subtle expressions.<n>Our model performed competitively, and in some cases, even better than state-of-the-art models on turn-taking metrics.
• Score: 0.9208007322096533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Turn-taking management is crucial for any social interaction. Still, it is challenging to model human-machine interaction due to the complexity of the social context and its multimodal nature. Unlike conventional systems based on silence duration, previous existing voice activity projection (VAP) models successfully utilized a unified representation of turn-taking behaviors as prediction targets, which improved turn-taking prediction performance. Recently, a multimodal VAP model outperformed the previous state-of-the-art model by a significant margin. In this paper, we propose a multimodal model enhanced with pre-trained audio and face encoders to improve performance by capturing subtle expressions. Our model performed competitively, and in some cases, even better than state-of-the-art models on turn-taking metrics. All the source codes and pretrained models are available at https://github.com/sagatake/VAPwithAudioFaceEncoders.

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