IRAF: Interference-Resilient Adaptive Fusion for Noise-Robust End-to-End Full-Duplex Spoken Dialogue Systems
arXiv:2606.06559v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models allow voice agents to listen and speak concurrently, enabling natural interaction with real-time overlap. However, end-to-end dual-channel models that jointly encode user and agent streams may degrade in realistic acoustic environments: interfering speakers leaking into the user microphone can be encoded as part of the user query, corrupting the LLM's conditioning and causing unstable turn-taking and reduced response quality. We propose Interference-Resilient Adaptive Fusion (IRAF), a lightweight, streaming-co
The proliferation of increasingly sophisticated LLMs and the demand for more natural, real-time voice interactions necessitate robust solutions for complex acoustic environments.
Improving noise robustness in full-duplex systems is critical for the widespread adoption and effectiveness of AI agents in real-world scenarios, particularly in human-computer interaction.
This advancement mitigates a significant technical hurdle in conversational AI, enabling more reliable and seamless verbal interaction with AI systems even in challenging audio conditions.
- · AI agents developers
- · Conversational AI companies
- · Speech recognition technology providers
- · Consumers of voice AI
- · Systems with poor noise resilience
- · Legacy voice interface providers
More accurate and natural voice interactions with AI across various devices and environments.
Accelerated integration of AI agents into daily tasks, from customer service to personal assistants, reducing friction in human-AI collaboration.
Increased reliance on voice as a primary interface for complex tasks, potentially shifting UI/UX paradigms away from traditional screens.
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Read at arXiv cs.AI