超越显著性：利用专家参考声学线索增强语音情感识别的可解释性
Beyond saliency: enhancing explanation of speech emotion recognition with expert-referenced acoustic cues
AI总结：
针对现有SER显著性解释方法无法关联有效情感声学标记的问题，本文提出结合专家参考声学线索的解释框架，可量化显著性区域线索幅值，提升SER模型解释的可理解性与合理性。
摘要：
面向语音情感识别（SER）的可解释人工智能（XAI）对于构建透明、可信的模型至关重要。当前改编自计算机视觉领域的基于显著性的方法虽能高亮谱图区域，却无法证明这些区域是否对应有意义的情感声学标记，限制了解释的忠实度与可解释性。我们提出一种框架，通过量化显著性区域内线索的幅值克服上述局限，明确高亮内容“是什么”并关联其重要性的“原因”，将显著性与语音情感的专家参考声学线索建立关联。在基准SER数据集上的实验表明，我们的方法通过将显著性区域与理论驱动的语音情感专家参考声学特征明确关联，提升了解释质量。与标准显著性方法相比，它能为SER模型提供更易懂、更合理的解释，为构建可信的基于语音的情感计算奠定基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
