Holistic perception of affective attributes is an important human perceptual ability. However, this ability is far from being realized in current affective computing, as not all of the attributes are well studied and their interrelationships are poorly understood. In this work, we investigate the relationship between two affective attributes: personality and emotion, from a transfer learning perspective. Specifically, we transfer Transformer-based and wav2vec2-based emotion recognition models to perceive personality from speech across corpora. Compared with previous studies, our results show that transferring emotion recognition is effective for personality perception. Moreoever, this allows for better use and exploration of small personality corpora. We also provide novel findings on the relationship between personality and emotion that will aid future research on holistic affect recognition.

翻译：情感属性的整体感知是人类重要的感知能力。然而，这一能力在当前情感计算领域远未实现，因为并非所有属性都得到充分研究，且其相互关系尚未明确。本研究从迁移学习视角探讨人格与情感两种情感属性之间的关系。具体而言，我们将基于Transformer和wav2vec2的情感识别模型迁移至跨语料库语音中的人格感知任务。与先前研究相比，结果表明情感识别迁移对人格感知具有有效性。此外，该方法有助于更好地利用和探索小型人格语料库。我们还提出了关于人格与情感关系的新发现，这将为未来整体情感识别研究提供支持。