OmniChat：利用可扩展的合成数据增强面向多样场景的语音对话系统
OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data for Diverse Scenarios
AI总结：
本文提出利用大规模合成数据构建 ShareChatX 数据集，并基于此设计多轮对话系统 OmniChat，通过异构特征融合优化多样场景下的对话能力，在 DailyTalk 上取得最先进结果。
摘要：
随着大语言模型的快速发展，研究者已经构建出越来越先进的语音对话系统，能够与人类自然对话。然而，这些系统仍然难以处理真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，主要原因是当前的对话数据集在规模和场景多样性上都受到限制。在本文中，我们提出利用合成数据来增强面向多样场景的对话模型。我们引入了 ShareChatX，这是第一个覆盖多样场景的综合性大规模语音对话数据集。基于该数据集，我们提出了 OmniChat，一个带有异构特征融合模块的多轮对话系统，旨在优化不同对话语境下的特征选择。此外，我们还探索了使用合成数据训练对话系统的关键问题。通过全面的实验，我们确定了合成数据与真实数据之间的理想平衡，在真实世界对话数据集 DailyTalk 上取得了最先进的结果。我们还强调了合成数据在处理多样、复杂对话场景中的关键重要性，尤其是涉及音频和音乐的场景。更多详情，请访问我们的演示页面：https://sharechatx.github.io/。
英文摘要：
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/}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
