AURA: Uncertainty-Routed Activation Editing for Acoustic Grounding in Speech Foundation Models
Attention encoder-decoder (AED) Speech Foundation Models achieve strong ASR performance but can generate acoustically unsupported text when inputs contain no speech, weak acoustic evidence, or unreliable transcription. We propose AURA: Activation-editing with Uncertainty-Routed Adaptation, an ultra-efficient representation-editing method that freezes the pretrained model and applies sparse scale-and-shift edits to decoder cross-attention heads. AURA dynamically routes edits using cross-attention uncertainty features that capture over-concentration, diffuse attention, and abrupt frame shifts. We evaluate AURA on four datasets spanning non-speech hallucination and speech grounding stressors...
| Authors | Natarajan Balaji Shankar, Zilai Wang, Zihan Wang et al. |
|---|---|
| Primary category | eess.AS |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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