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 wav2vec-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，当前情感计算领域远未实现这一能力，因为并非所有属性都得到充分研究，且其相互关系尚不明确。本文从迁移学习视角探究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wav2vec的情感识别模型迁移至跨语料库的语音人格感知任务。与先前研究相比，实验结果表明情感识别迁移对人格感知具有有效性，且有助于更好地利用和探索小规模人格语料库。此外，本研究揭示了人格与情感关系的新发现，将为未来整体情感识别研究提供重要参考。