MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild
Abstract
Current multiparty turn-taking models often rely on complex microphone arrays or multi-camera setups, limiting their applicability in human-robot interaction scenarios. We introduce MuVAP, a causal multimodal framework that extends Voice Activity Projection by grounding acoustic predictions in face tracks, enabling speaker-aware turn-taking predictions from a monaural audio stream and a single camera view. To address the combinatorial complexity of modeling multiple speakers, we propose Role-Relative Projection, which maps any N-speaker interaction onto a fixed current versus next floor-holder state. Because existing audiovisual datasets contain disruptive editing cuts that break causal tracking, we introduce the Audio-Visual Conversation Corpus, a 31-hour dataset of unedited, single-camera multiparty conversations. Evaluations demonstrate that MuVAP outperforms strong baselines on Shift-Hold and next-speaker prediction tasks across two- and three-speaker settings.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of MuVAP, a causal multimodal framework that effectively predicts turn-taking in multiparty conversations using a single audio stream and a single camera view. This innovative approach, combined with the creation of the AVCC dataset, addresses critical limitations in existing methods and has the potential to advance the field of conversational AI significantly.
Methodology
The paper presents MuVAP, a novel multimodal framework that integrates audio and visual data to predict turn-taking in multiparty conversations. The methodology is well-structured, introducing Role-Relative Projection to simplify the complexity of multiparty interactions by focusing on the current and next speaker. The use of a single audio channel and a single camera view is a significant departure from traditional methods, which often require complex setups. The introduction of the Audio-Visual Conversation Corpus (AVCC) is a crucial contribution, as it provides a dataset specifically designed for this type of analysis, addressing the limitations of existing datasets.
Results & evaluation
The experiments are comprehensive, comparing MuVAP against strong baselines across various tasks, including Shift-Hold and Next Speaker Prediction. The results demonstrate that MuVAP outperforms these baselines, showcasing its effectiveness in real-world scenarios. The evaluation metrics used, such as Macro-F1 for Shift-Hold Prediction and accuracy for Next Speaker Prediction, are appropriate for the tasks at hand. The paper provides detailed results and analysis, indicating a thorough evaluation process.
Reproducibility
The paper includes sufficient implementation details, including the architecture of the model, training procedures, and the datasets used. However, the lack of a public demo or clear access to the trained models may hinder full reproducibility. The GitHub repository provides some resources, but additional documentation would enhance reproducibility.
Limitations
The paper acknowledges limitations, such as the class imbalance introduced by the Role-Relative Projection and the reliance on visual tracking that may miss subtle facial cues. Additionally, the model's performance is evaluated primarily on two- and three-speaker settings, which may not fully represent larger group dynamics. The authors also note the potential for improved performance with more advanced visual encoders.
Broader impact
The implications of this research are significant for human-robot interaction and conversational AI, as it enables more natural and responsive interactions in multiparty settings. The ability to predict turn-taking dynamics using standard hardware makes this approach accessible for various applications, including social robotics, virtual assistants, and interactive media. The main contribution of this paper is the introduction of MuVAP, a causal multimodal framework that effectively predicts turn-taking in multiparty conversations using a single audio stream and a single camera view. This innovative approach, combined with the creation of the AVCC dataset, addresses critical limitations in existing methods and has the potential to advance the field of conversational AI significantly.
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