# Voice Activity Projection Model with Multimodal Encoders

arXiv: https://arxiv.org/abs/2506.03980 · HTML: https://mlsanity.com/paper/2506.03980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Moshi: a speech-text foundation model for real-time dialogue](https://mlsanity.com/paper/2410.00037/) (2024)
- [Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming](https://mlsanity.com/paper/2408.16725/) (2024)
- [Unsupervised pretraining transfers well across languages](https://mlsanity.com/paper/2002.02848/) (2020)
- [Voice Activity Projection: Self-supervised Learning of Turn-taking Events](https://mlsanity.com/paper/2205.09812/) (2022)
- [Multilingual Turn-taking Prediction Using Voice Activity Projection](https://mlsanity.com/paper/2403.06487/) (2024)
- [How Much Does Prosody Help Turn-taking? Investigations using Voice Activity Projection Models](https://mlsanity.com/paper/2209.05161/) (2022)
- [Real-time and Continuous Turn-taking Prediction Using Voice Activity Projection](https://mlsanity.com/paper/2401.04868/) (2024)
- [Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion](https://mlsanity.com/paper/2401.14717/) (2024)
- [Yeah, Un, Oh: Continuous and Real-time Backchannel Prediction with Fine-tuning of Voice Activity Projection](https://mlsanity.com/paper/2410.15929/) (2024)

## Built on by (most cited first, 2 of 2)

- [Multimodal Voice Activity Projection for Turn-Taking in Social Robots with Voice-Activity-Related Pretrained Encoders](https://mlsanity.com/paper/2607.07294/) (2026)
- [Evaluating Real-Time Voice Agents: From Component Quality to Grounded Outcomes](https://mlsanity.com/paper/2609.30798/) (2026)

## Abstract (arXiv)

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.