This study investigates the interaction between personality traits and emotion expression, exploring how personality information can improve speech emotion recognition (SER). We collect the personality annotation for the IEMOCAP dataset, making it the first speech dataset that contains both emotion and personality annotations (PA-IEMOCAP), and enabling direct integration of personality traits into SER. Statistical analysis on this dataset identified significant correlations between personality traits and emotional expressions. To extract finegrained personality features, we propose a temporal interaction condition network (TICN), in which personality features are integrated with HuBERT-based acoustic features for SER. Experiments show that incorporating ground-truth personality traits significantly enhances valence recognition, improving the concordance correlation coefficient (CCC) from 0.698 to 0.785 compared to the baseline without personality information. For practical applications in dialogue systems where personality information about the user is unavailable, we develop a front-end module of automatic personality recognition. Using these automatically predicted traits as inputs to our proposed TICN model, we achieve a CCC of 0.776 for valence recognition, representing an 11.17% relative improvement over the baseline. These findings confirm the effectiveness of personality-aware SER and provide a solid foundation for further exploration in personality-aware speech processing applications.

翻译：本研究探讨人格特质与情感表达之间的相互作用，探索如何利用人格信息提升语音情感识别（SER）性能。我们为IEMOCAP数据集收集了人格标注，使其成为首个同时包含情感与人格标注的语音数据集（PA-IEMOCAP），从而实现了人格特质与SER的直接融合。对该数据集的统计分析揭示了人格特质与情感表达之间的显著相关性。为提取细粒度人格特征，我们提出了一种时间交互条件网络（TICN），该网络将人格特征与基于HuBERT的声学特征相结合用于SER。实验表明，引入真实人格特质可显著提升效价识别性能，与未使用人格信息的基线相比，一致性相关系数（CCC）从0.698提高至0.785。针对对话系统中用户人格信息未知的实际应用场景，我们开发了自动人格识别前端模块。使用这些自动预测的人格特征作为TICN模型的输入，我们在效价识别任务中取得了0.776的CCC，相较于基线实现了11.17%的相对性能提升。这些发现证实了人格感知SER的有效性，并为深入探索人格感知语音处理应用奠定了坚实基础。