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

• URL: http://arxiv.org/abs/2511.11691v1
• Date: Wed, 12 Nov 2025 09:40:36 GMT
• Title: Beyond saliency: enhancing explanation of speech emotion recognition with expert-referenced acoustic cues
• Authors: Seham Nasr, Zhao Ren, David Johnson,
• Abstract summary: Current saliency-based methods, adapted from vision, highlight spectrogram regions but fail to show whether these regions correspond to meaningful acoustic markers of emotion.<n>We propose a framework that overcomes these limitations by quantifying the magnitudes of cues within salient regions.<n>This clarifies "what" is highlighted and connects it to "why" it matters, linking saliency to expert-referenced acoustic cues of speech emotions.
• Score: 5.597645495963195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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