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This research introduces LEAF-X, a novel framework for transformer-based automatic speech recognition (ASR) that enhances model interpretability through entropy-guided attention mechanisms. By focusing on low-entropy heads and layers, the method achieves significantly improved faithfulness and temporal grounding in explanations compared to standard perturbation techniques or raw attention maps. These advancements offer critical transparency for deploying auditable ASR systems where understanding internal decision logic is essential.
Listening with Attention: Entropy-Guided Explainability for Transformer-Based Audio Models
arXiv:2606.14647v1 Announce Type: cross Abstract: Transformer-based automatic speech recognition (ASR) models such as Whisper are highly accurate, but their predictions remain difficult to interpret. Existing explainable AI (XAI) methods often lack faithfulness and precise temporal grounding. We propose Listening with Entropy-guided Attention for Faithful explainability (LEAF-X), a model-intrinsic XAI framework for transformer-based ASR. LEAF-X combines entropy-guided attention weighting, multi-layer attention rollout, and optional causal ablations to identify low-entropy, high-impact heads and layers, producing sparse token-to-frame attributions. Unlike perturbation-based explainers or raw attention maps, LEAF-X exploits the internal structure of encoder-decoder and speech-augmented decoder-only models to generate explanations that better reflect model computation. Results show 32% improved faithfulness, 35-39% stronger locality/sparsity, and the most stable attributions, supporting more transparent and auditable ASR.
Abstract:Transformer-based automatic speech recognition (ASR) models such as Whisper are highly accurate, but their predictions remain difficult to interpret. Existing explainable AI (XAI) methods often lack faithfulness and precise temporal grounding. We propose Listening with Entropy-guided Attention for Faithful explainability (LEAF-X), a model-intrinsic XAI framework for transformer-based ASR. LEAF-X combines entropy-guided attention weighting, multi-layer attention rollout, and optional causal ablations to identify low-entropy, high-impact heads and layers, producing sparse token-to-frame attributions. Unlike perturbation-based explainers or raw attention maps, LEAF-X exploits the internal structure of encoder-decoder and speech-augmented decoder-only models to generate explanations that better reflect model computation. Results show 32% improved faithfulness, 35-39% stronger locality/sparsity, and the most stable attributions, supporting more transparent and auditable ASR.
17 pages, 3 figures, and 9 tables. Accepted in Interspeech 2026 conference
Sound (cs.SD); Artificial Intelligence (cs.AI)
arXiv:2606.14647 [cs.SD]
(or arXiv:2606.14647v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2606.14647 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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