Auditory foundation models, including auditory large language models (LLMs), process all sound inputs equally, independent of listener perception. However, human auditory perception is inherently selective: listeners focus on specific speakers while ignoring others in complex auditory scenes. Existing models do not incorporate this selectivity, limiting their ability to generate perception-aligned responses. To address this, we introduce Intention-Informed Auditory Scene Understanding (II-ASU) and present Auditory Attention-Driven LLM (AAD-LLM), a prototype system that integrates brain signals to infer listener attention. AAD-LLM extends an auditory LLM by incorporating intracranial electroencephalography (iEEG) recordings to decode which speaker a listener is attending to and refine responses accordingly. The model first predicts the attended speaker from neural activity, then conditions response generation on this inferred attentional state. We evaluate AAD-LLM on speaker description, speech transcription and extraction, and question answering in multitalker scenarios, with both objective and subjective ratings showing improved alignment with listener intention. By taking a first step toward intention-aware auditory AI, this work explores a new paradigm where listener perception informs machine listening, paving the way for future listener-centered auditory systems. Demo and code available:this https URL.
View on arXiv@article{jiang2025_2502.16794,
title={ AAD-LLM: Neural Attention-Driven Auditory Scene Understanding },
author={ Xilin Jiang and Sukru Samet Dindar and Vishal Choudhari and Stephan Bickel and Ashesh Mehta and Guy M McKhann and Daniel Friedman and Adeen Flinker and Nima Mesgarani },
journal={arXiv preprint arXiv:2502.16794},
year={ 2025 }
}