Fine-tuning Wav2vec for Vocal-burst Emotion Recognition
This work addresses emotion recognition for affective computing, but it is incremental as it applies an existing method (fine-tuning Wav2vec) to a new dataset.
The authors tackled emotion recognition from non-verbal vocal bursts like laughs and cries, achieving promising results compared to the baseline in the A-VB competition.
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.