Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
Abstract
Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based agents, particularly under dynamic floor competition and varying user expectations. We propose ModeratorLM, a role-playing voice agent that conditions turn-taking behavior on an explicitly assigned role in multi-party settings. The system is built on a speech large language model operating in chunk-wise streaming manner. We further introduce a reasoning-augmented variant that incorporates chain-of-thought reasoning over conversational context and the assigned role. We construct RolePlayConv, a large-scale synthetic dataset of spoken multi-party conversations with diverse assistant roles. Experiments on real-world meeting data and RolePlayConv show improved turn-taking precision by over 40% and recall by more than 70%, while substantially reducing false-positive interruptions compared to non-role-conditioned baselines.
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 ModeratorLM, a role-playing voice agent that enhances turn-taking in multi-party conversations through role conditioning and reasoning. This work represents a significant advancement in the field of conversational AI, addressing a critical challenge in multi-party interactions and providing a novel dataset for future research.
Methodology
The proposed methodology introduces ModeratorLM, a role-playing voice agent that utilizes a speech large language model (LLM) to manage turn-taking in multi-party conversations. The approach is innovative in its use of role conditioning to influence turn-taking behavior, which is a significant advancement over traditional models that do not consider role dynamics. The integration of chain-of-thought reasoning in the ModeratorLM-Think variant adds an additional layer of sophistication, allowing the model to better interpret conversational context. The construction of the RolePlayConv dataset is also a notable contribution, as it provides a tailored resource for training and evaluating role-conditioned agents in multi-party settings. However, the reliance on synthetic data may raise questions about the generalizability of the findings.
Results & evaluation
The experiments conducted demonstrate a clear improvement in turn-taking precision and recall when using the ModeratorLM models compared to non-role-conditioned baselines. The use of both real-world meeting data and the synthetic RolePlayConv dataset strengthens the evaluation. The metrics reported, including precision, recall, F1-score, and reactive miss rate, provide a comprehensive view of the model's performance. The ablation studies further validate the importance of dynamic chunking and the role of reasoning in enhancing model performance. However, the lack of extensive human evaluations beyond the small-scale study may limit the robustness of the claims regarding role fidelity.
Reproducibility
The paper provides a detailed description of the training and evaluation setup, including the architecture of the models, the dataset construction process, and the evaluation metrics. However, there is no mention of code or data availability, which is crucial for reproducibility in machine learning research. The absence of a demo or project URL also hinders the ability for others to replicate the work.
Limitations
One significant limitation is the reliance on synthetic data for training the RolePlayConv dataset, which may not fully capture the complexities of real-world multi-party conversations. Additionally, while the model shows improved performance in turn-taking, it remains conservative, missing some valid response opportunities, which could affect user experience in practical applications. The paper does not address potential biases in the dataset or the model's performance across diverse demographics.
Broader impact
The development of role-conditioned voice agents has the potential to significantly enhance the usability of conversational AI in various applications, such as virtual assistants, customer service, and collaborative tools. By improving turn-taking behavior, these agents can facilitate more natural and effective interactions in multi-party settings. However, ethical considerations regarding the deployment of such technology, especially in sensitive contexts, must be carefully evaluated. The main contribution of this paper is the introduction of ModeratorLM, a role-playing voice agent that enhances turn-taking in multi-party conversations through role conditioning and reasoning. This work represents a significant advancement in the field of conversational AI, addressing a critical challenge in multi-party interactions and providing a novel dataset for future research.
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