  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.
