标题： OmniChat：通过可扩展的合成数据增强多样化场景的口语对话系统 显示英文标题

标题： OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data for Diverse Scenarios

摘要： 随着大型语言模型的快速发展，研究人员已经创建了越来越先进的语音对话系统，这些系统可以自然地与人类进行对话。 然而，这些系统仍然难以处理现实世界对话的全部复杂性，包括音频事件、音乐情境和情感表达，主要是因为当前的对话数据集在规模和场景多样性方面受到限制。 在本文中，我们提出利用合成数据来增强跨不同场景的对话模型。 我们引入了ShareChatX，这是第一个全面的、大规模的语音对话数据集，涵盖了多种场景。 基于这个数据集，我们介绍了OmniChat，一个具有异构特征融合模块的多轮对话系统，旨在优化不同对话情境中的特征选择。 此外，我们探讨了使用合成数据训练对话系统的关键方面。 通过全面的实验，我们确定了合成数据和真实数据之间的理想平衡，在真实世界对话数据集DailyTalk上实现了最先进的结果。 我们还强调了合成数据在应对多样化、复杂对话场景中的重要性，尤其是涉及音频和音乐的场景。 有关更多细节，请访问我们的演示页面 at \url{https://sharechatx.github.io/}. 显示英文摘要

摘要： With the rapid development of large language models, researchers have created increasingly advanced spoken dialogue systems that can naturally converse with humans. However, these systems still struggle to handle the full complexity of real-world conversations, including audio events, musical contexts, and emotional expressions, mainly because current dialogue datasets are constrained in both scale and scenario diversity. In this paper, we propose leveraging synthetic data to enhance the dialogue models across diverse scenarios. We introduce ShareChatX, the first comprehensive, large-scale dataset for spoken dialogue that spans diverse scenarios. Based on this dataset, we introduce OmniChat, a multi-turn dialogue system with a heterogeneous feature fusion module, designed to optimize feature selection in different dialogue contexts. In addition, we explored critical aspects of training dialogue systems using synthetic data. Through comprehensive experimentation, we determined the ideal balance between synthetic and real data, achieving state-of-the-art results on the real-world dialogue dataset DailyTalk. We also highlight the crucial importance of synthetic data in tackling diverse, complex dialogue scenarios, especially those involving audio and music. For more details, please visit our demo page at \url{https://sharechatx.github.io/}.