Speech Emotion Recognition Using Fine-Tuned DWFormer:A Study on Track 1 of the IERPChallenge 2024

Abstract

The field of artificial intelligence has a strong interest in the topic of emotion recognition. The majority of extant emotion recognition models are oriented towards enhancing the precision of discrete emotion label prediction. Given the direct relationship between human personality and emotion, as well as the significant inter-individual differences in subjective emotional expression, the IERP Challenge 2024 incorporates personality traits into emotion recognition research. This paper presents the Fosafer submissions to the Track 1 of the IERP Challenge 2024. This task primarily concerns the recognition of emotions in audio, while also providing text and audio features. In Track 1, we utilized exclusively audio-based features and fine-tuned a pre-trained speech emotion recognition model, DWFormer, through the integration of data augmentation and score fusion strategies, thereby achieving the first place among the participating teams.