I'll Keep an Ear Out: Teaching AudioLLMs Proactive Audio Assistance

2026-09-17

AI Summary

研究解决了音频大模型仅被动响应的问题，通过引入Interrupt和Silent Modeling方法实现主动音频辅助，提高对聋哑用户群体的实用性。

Abstract

Audio large language models (AudioLLMs) operate reactively, responding only when queried. We introduce proactive audio assistance, where an AudioLLM monitors an audio stream and autonomously decides when to alert the user from a single natural-language intent, motivated by wearable applications for Deaf and Hard of Hearing users. We propose Interrupt and Silent Modeling (ISM), a model-agnostic paradigm that embeds proactive decisions into LLM decoding via two special tokens: \texttt{<interrupt>} and \texttt{<silent>}, capturing four states: onset detection, sustained-relevance triggering, irrelevance suppression, and de-duplication. Applied to Qwen2-Audio-7B, ISM achieves 99.6\% interrupt F1 and perfect de-duplication recall on ESC-50. On noisy Epic-Sounds kitchen audio, ISM achieves the highest interrupt F1 without domain-specific training, the only method maintaining strong onset detection without over-triggering or over-suppression. Streaming evaluation confirms real-time viability with 3.5-second average latency.

Problem

• AudioLLMs
• Proactive Audio Assistance
• Wearable Applications

Innovation

• proactive audio assistance
• Interrupt and Silent Modeling (ISM)
• audio stream monitoring