Abstract
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.
Abstract (translated)
情感属性的全面感知是一项重要的人类感知能力。然而，在当前的情感计算中，这一能力尚未得到实现，因为不是所有的属性都得到充分研究，并且它们之间的相互关系也未被很好地理解。在本研究中，我们从迁移学习的角度出发，研究人格和情感两个情感属性之间的关系。具体来说，我们迁移了基于Transformer和wav2vec的情感识别模型，从多个语料库中识别人格。与以前的研究相比，我们的结果表明，迁移情感识别对于人格感知非常有效。此外，这还使得更有效地利用和探索小型人格语料库更加方便。我们还研究了人格和情感之间的关系，并提出了一些新发现，这将为全面情感识别研究提供有益的启示。
URL
https://arxiv.org/abs/2305.16076
