# Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts

arXiv: https://arxiv.org/abs/2209.07384 · HTML: https://mlsanity.com/paper/2209.07384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)
- [Auxiliary Tasks in Multi-task Learning](https://mlsanity.com/paper/1805.06334/) (2018)
- [The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts](https://mlsanity.com/paper/2205.01780/) (2022)
- [The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression](https://mlsanity.com/paper/2207.03572/) (2022)
- [Redundancy Reduction Twins Network: A Training framework for Multi-output Emotion Regression](https://mlsanity.com/paper/2206.09142/) (2022)
- [Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations](https://mlsanity.com/paper/2206.10695/) (2022)
- [Exploring speaker enrolment for few-shot personalisation in emotional vocalisation prediction](https://mlsanity.com/paper/2206.06680/) (2022)
- [Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression](https://mlsanity.com/paper/2206.11049/) (2022)
- [Jointly Predicting Emotion, Age, and Country Using Pre-Trained Acoustic Embedding](https://mlsanity.com/paper/2207.10333/) (2022)

## Built on by (most cited first, 1 of 1)

- [The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data](https://mlsanity.com/paper/2403.14048/) (2024)

## Abstract (arXiv)

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.