# Transfer Learning for Personality Perception via Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2305.16076 · HTML: https://mlsanity.com/paper/2305.16076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2023.

## Built on by (most cited first, 1 of 1)

- [Speech foundation models in healthcare: Effect of layer selection on pathological speech feature prediction](https://mlsanity.com/paper/2402.01796/) (2024)

## Abstract (arXiv)

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.