Prior research indicates that users prefer assistive technologies whose personalities align with their own. This has sparked interest in automatic personality perception (APP), which aims to predict an individual's perceived personality traits. Previous studies in APP have treated personalities as static traits, independent of context. However, perceived personalities can vary by context and situation as shown in psychological research. In this study, we investigate the relationship between conversational speech and perceived personality for participants engaged in two work situations (a neutral interview and a stressful client interaction). Our key findings are: 1) perceived personalities differ significantly across interactions, 2) loudness, sound level, and spectral flux features are indicative of perceived extraversion, agreeableness, conscientiousness, and openness in neutral interactions, while neuroticism correlates with these features in stressful contexts, 3) handcrafted acoustic features and non-verbal features outperform speaker embeddings in inference of perceived personality, and 4) stressful interactions are more predictive of neuroticism, aligning with existing psychological research.

翻译：先前研究表明，用户倾向于选择与自己人格特质相匹配的辅助技术。这一发现激发了对自动人格感知（APP）研究的兴趣，其旨在预测个体被感知到的人格特质。以往APP研究将人格视为独立于情境的静态特征，然而心理学研究显示，感知到的人格会随情境和场景而变化。本研究聚焦于参与两种工作场景（中性面试与高压客户互动）的受试者，探究对话语音与被感知人格之间的关联。主要发现如下：1）不同互动情境下感知到的人格存在显著差异；2）响度、声压级和频谱通量等特征在中性互动中可指示外倾性、宜人性、尽责性和开放性，而神经质则在高压情境下与这些特征相关；3）手工声学特征与非语言特征在推断被感知人格方面优于说话者嵌入向量；4）高压互动对神经质的预测性更强，这与现有心理学研究结论一致。