Listening with Attention: Entropy-Guided Explainability for Transformer-Based Audio Models

Automatic speech recognition models like Whisper are impressively accurate, but when they fail — or when accountability matters — we rarely know why they made a particular decision. LEAF-X introduces a principled explainability framework that uses entropy patterns in attention heads to identify which audio frames most influenced a transcription. It produces sparser, more faithful attributions than existing methods, with 32% better faithfulness scores. Practical applications include auditable transcription systems for legal or medical settings, debugging ASR failures in edge cases like accented speech or noisy environments, and building regulatory-compliant voice AI where model decisions must be traceable and explainable to non-technical stakeholders. Authors: Ravi Ranjan, Utkarsh Grover, Xiaomin Lin, Agoritsa Polyzou Paper: https://arxiv.org/abs/2606.14647v1