# The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression

arXiv: https://arxiv.org/abs/2207.03572 · HTML: https://mlsanity.com/paper/2207.03572/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts](https://mlsanity.com/paper/2205.01780/) (2022)
- [End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning](https://mlsanity.com/paper/1802.01115/) (2018)

## Built on by (most cited first, 7 of 7)

- [Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts](https://mlsanity.com/paper/2209.07384/) (2022)
- [Self-Relation Attention and Temporal Awareness for Emotion Recognition via Vocal Burst](https://mlsanity.com/paper/2209.07629/) (2022)
- [An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysis](https://mlsanity.com/paper/2209.13914/) (2022)
- [Fine-tuning Wav2vec for Vocal-burst Emotion Recognition](https://mlsanity.com/paper/2210.00263/) (2022)
- [EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies](https://mlsanity.com/paper/2301.00508/) (2023)
- [A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition](https://mlsanity.com/paper/2303.08027/) (2023)
- [The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data](https://mlsanity.com/paper/2403.14048/) (2024)

## Abstract (arXiv)

The ACII Affective Vocal Bursts Workshop & Competition is focused on understanding multiple affective dimensions of vocal bursts: laughs, gasps, cries, screams, and many other non-linguistic vocalizations central to the expression of emotion and to human communication more generally. This year's competition comprises four tracks using a large-scale and in-the-wild dataset of 59,299 vocalizations from 1,702 speakers. The first, the A-VB-High task, requires competition participants to perform a multi-label regression on a novel model for emotion, utilizing ten classes of richly annotated emotional expression intensities, including; Awe, Fear, and Surprise. The second, the A-VB-Two task, utilizes the more conventional 2-dimensional model for emotion, arousal, and valence. The third, the A-VB-Culture task, requires participants to explore the cultural aspects of the dataset, training native-country dependent models. Finally, for the fourth task, A-VB-Type, participants should recognize the type of vocal burst (e.g., laughter, cry, grunt) as an 8-class classification. This paper describes the four tracks and baseline systems, which use state-of-the-art machine learning methods. The baseline performance for each track is obtained by utilizing an end-to-end deep learning model and is as follows: for A-VB-High, a mean (over the 10-dimensions) Concordance Correlation Coefficient (CCC) of 0.5687 CCC is obtained; for A-VB-Two, a mean (over the 2-dimensions) CCC of 0.5084 is obtained; for A-VB-Culture, a mean CCC from the four cultures of 0.4401 is obtained; and for A-VB-Type, the baseline Unweighted Average Recall (UAR) from the 8-classes is 0.4172 UAR.