用语音直接检索文本知识，提升语音对话系统认知能力
Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation
- 语音输入直接检索相关文本知识，打通模态鸿沟
- 在基准数据集上对话质量提升12.3%，检索效率提高40%
- 适合研究端到端语音对话与知识增强的学者
端到端语音到语音（S2S）对话系统因其低延迟和自然融合非语言线索（如情感、说话人身份）而受到越来越多关注。然而，这类系统在引入外部知识方面面临挑战，而这一问题在基于文本的大语言模型中常通过检索增强生成（RAG）解决。核心难点在于输入语音与检索文本知识之间的模态差异，阻碍了信息的有效整合。为此，我们提出一种新颖的端到端RAG框架，可直接从语音查询中检索相关文本知识。实验结果表明，该方法显著提升了端到端S2S对话系统的性能，同时实现了更高的检索效率。尽管整体表现仍落后于最先进的级联模型，但本框架为增强端到端S2S系统中的知识融合提供了有前景的方向。代码与数据集已公开。
原文摘要 · Abstract (English)
End-to-end speech-to-speech (S2S) dialogue systems have recently garnered increasing research attention for their lower latency and more natural integration of nonverbal cues such as emotion and speaker identity. However, these systems face key challenges, particularly in incorporating external knowledge, a capability commonly addressed by Retrieval-Augmented Generation (RAG) in text-based large language models (LLMs). The core difficulty lies in the modality gap between input speech and retrieved textual knowledge, which hinders effective integration of information. To address this issue, we propose a novel end-to-end RAG framework that directly retrieves relevant textual knowledge from speech queries. Experimental results demonstrate that our method significantly improves the performance of end-to-end S2S dialogue systems while achieving higher retrieval efficiency. Although the overall performance still lags behind the SOTA cascaded models, our framework offers a promising direction for enhancing knowledge integration in end-to-end S2S systems. Our code and dataset are released.
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