Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts
This work addresses emotion recognition from vocal bursts, which is important for improving speech emotion recognition systems, but it appears incremental as it builds on existing methods for a specific challenge.
The paper tackled multi-task emotion recognition on vocal bursts by using a self-supervised audio model with attention networks and uncertainty loss weighting, surpassing the challenge baseline by a wide margin on all four tasks.
Vocal bursts play an important role in communicating affect, making them valuable for improving speech emotion recognition. Here, we present our approach for classifying vocal bursts and predicting their emotional significance in the ACII Affective Vocal Burst Workshop & Challenge 2022 (A-VB). We use a large self-supervised audio model as shared feature extractor and compare multiple architectures built on classifier chains and attention networks, combined with uncertainty loss weighting strategies. Our approach surpasses the challenge baseline by a wide margin on all four tasks.