SER研究动机与实际数据脱节，可能引发伦理风险。
Unrequited Emotions: Investigating the Gaps in Motivation and Practice in Speech Emotion Recognition Research
- 通过系统调研发现动机与实践不一致
- 常用数据集未反映声称的应用场景
- 建议明确具体应用场景以避免误用
对情感识别技术的批判性分析已引发关于任务有效性和潜在下游影响的伦理担忧，呼吁研究人员确保其研究动机与实践相一致。然而，这些讨论尚未充分影响或借鉴语音情感识别（SER）领域的研究。本文通过系统调查SER研究，揭示了该领域宣称的研究动机与其所使用的数据集和情感类别之间的不匹配。尽管许多研究提出诸如语音助手或医疗应用等有吸引力的目标，但常用数据集并未反映这些实际部署情境，导致动机与实践之间存在明显差距。我们指出，这种脱节可能引发伦理问题，并主张SER研究应重新确立具体应用场景，以防止误解、误用及后续危害。
原文摘要 · Abstract (English)
Critical analyses of emotion recognition technology have raised ethical concerns around task validity and potential downstream impacts, urging researchers to ensure alignment between their stated motivations and practice. However, these discussions have not adequately influenced or drawn from research on speech emotion recognition (SER). We address this gap by conducting a systematic survey of SER research to uncover what stated motivations drive this work and if they align with the datasets and emotions studied. We find that while SER research identifies appealing goals, such as well-situated voice-activated systems or healthcare applications, commonly-used datasets do not reflect these proposed deployment contexts, thus presenting a gap between motivations and research practices. We argue that such gaps engender ethical concerns, and that SER research should reassert itself with concrete use-cases to prevent misinterpretations, misuse, and downstream harms.
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