Transfer Learning for Personality Perception via Speech Emotion Recognition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProc. INTERSPEECH 2023
PublisherInternational Speech Communication Association
Number of pages5
Publication statusPublished - 20 Aug 2023
EventInterspeech 2023 - Dublin, Ireland
Duration: 20 Aug 202324 Aug 2023
Conference number: 24

Publication series

ISSN (Electronic)1990-9772


ConferenceInterspeech 2023
Internet address

Keywords / Materials (for Non-textual outputs)

  • personality perception
  • emotion recognition
  • Transformer
  • wav2vec2
  • transfer learning
  • data augmentation


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