Fine-tuning Wav2vec for Vocal-burst Emotion Recognition

TL;DR

This paper explores fine-tuning Wav2vec for recognizing emotions from non-verbal vocal bursts like laughs and cries, demonstrating promising results in a new affective computing challenge.

Contribution

It introduces a fine-tuning approach of Wav2vec for vocal-burst emotion recognition, a novel application in affective computing.

Findings

Achieved promising results compared to baseline models

Demonstrated effectiveness of fine-tuned Wav2vec for emotion recognition

Contributed to the new A-VB competition tasks

Abstract

The ACII Affective Vocal Bursts (A-VB) competition introduces a new topic in affective computing, which is understanding emotional expression using the non-verbal sound of humans. We are familiar with emotion recognition via verbal vocal or facial expression. However, the vocal bursts such as laughs, cries, and signs, are not exploited even though they are very informative for behavior analysis. The A-VB competition comprises four tasks that explore non-verbal information in different spaces. This technical report describes the method and the result of SclabCNU Team for the tasks of the challenge. We achieved promising results compared to the baseline model provided by the organizers.