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arXiv · 2511.11691

Beyond saliency: enhancing explanation of speech emotion recognition with expert-referenced acoustic cues

Seham Nasr·Zhao Ren·David Johnson
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Abstract

Explainable AI (XAI) for Speech Emotion Recognition (SER) is critical for building transparent, trustworthy models. Current saliency-based methods, adapted from vision, highlight spectrogram regions but fail to show whether these regions correspond to meaningful acoustic markers of emotion, limiting faithfulness and interpretability. We propose a framework that overcomes these limitations by quantifying the magnitudes of cues within salient regions. This clarifies "what" is highlighted and connects it to "why" it matters, linking saliency to expert-referenced acoustic cues of speech emotions. Experiments on benchmark SER datasets show that our approach improves explanation quality by explicitly linking salient regions to theory-driven speech emotions expert-referenced acoustics. Compared to standard saliency methods, it provides more understandable and plausible explanations of SER models, offering a foundational step towards trustworthy speech-based affective computing.

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Seham Nasr, Zhao Ren, David Johnson. 2025-11-12. Beyond saliency: enhancing explanation of speech emotion recognition with expert-referenced acoustic cues. https://arxiv.org/abs/2511.11691

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