Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM

The recent publication on arXiv introduces a novel end-to-end framework for speech-to-speech (S2S) dialogue systems, focusing on enhancing knowledge integration through Retrieval-Augmented Generation (RAG). This framework aims to overcome the challenges of incorporating external knowledge, which is crucial for improving the naturalness and efficiency of dialogue systems. Experimental results indicate that this approach significantly boosts performance and retrieval efficiency, marking a promising advancement in the field. Despite these improvements, the framework's overall performance still lags behind existing state-of-the-art (SOTA) cascaded models, highlighting the ongoing challenges in the integration of speech and text modalities. The release of the accompanying code and dataset is expected to facilitate further exploration and development in this area, potentially paving the way for more sophisticated dialogue systems that can better understand and respond to user inputs.

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