如何与用户一起探索语音情感人工智能中的偏见？一项探索年龄与语言偏见的语音情感表演研究
"How to Explore Biases in Speech Emotion AI with Users?" A Speech-Emotion-Acting Study Exploring Age and Language Biases
AI总结：
本研究通过让青少年和55岁以上成人用丹麦语和英语进行有意识的语音情感表演，结合实时SER预测界面，发现模型解释在年龄和语言上具有一定稳健性，并呼吁构建更具包容性、以人为中心的语音情感识别模型。
摘要：
本研究探讨年龄和语言如何塑造有意识的声音情感表达，针对语音情感识别（SER）中研究不足的用户群体，即青少年（N = 12）和55岁以上成年人（N = 12）。虽然大多数SER系统是在自发的单语英语数据上训练的，但我们的研究评估了此类模型如何解释跨年龄组和跨语言（丹麦语和英语）的有意表演情感语音。为此，我们开发了一种新颖的实验范式，将定制用户界面与用于实时SER预测和数据记录的后端相结合。参与者被提示通过在效价-唤醒空间中击中视觉目标，有意识地表达四种情感目标。虽然局限性包括在一定程度上依赖自我管理的语音录音以及任务执行不一致，但结果表明，与预期相反，语言或年龄组之间没有显著差异，并且模型解释表现出一定程度的跨语言和跨年龄稳健性。不过，在高唤醒度情感识别方面存在一些明显的局限性。我们的定性发现强调，需要超越以系统为中心的准确率指标，并采用更具包容性、以人为中心的SER模型。通过将情感表达框定为一种目标导向行为，并记录人类意图与机器解释之间的实时差距，我们揭示了情感错位的风险。
英文摘要：
This study explores how age and language shape the deliberate vocal expression of emotion, addressing underexplored user groups, Teenagers (N = 12) and Adults 55+ (N = 12), within speech emotion recognition (SER). While most SER systems are trained on spontaneous, monolingual English data, our research evaluates how such models interpret intentionally performed emotional speech across age groups and languages (Danish and English). To support this, we developed a novel experimental paradigm combining a custom user interface with a backend for real-time SER prediction and data logging. Participants were prompted to hit visual targets in valence-arousal space by deliberately expressing four emotion targets. While limitations include some reliance on self-managed voice recordings and inconsistent task execution, the results suggest contrary to expectations, no significant differences between language or age groups, and a degree of cross-linguistic and age robustness in model interpretation. Though some limitations in high-arousal emotion recognition were evident. Our qualitative findings highlight the need to move beyond system-centered accuracy metrics and embrace more inclusive, human-centered SER models. By framing emotional expression as a goal-directed act and logging the real-time gap between human intent and machine interpretation, we expose the risks of affective misalignment.
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。
