# The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts

arXiv: https://arxiv.org/abs/2205.01780 · HTML: https://mlsanity.com/paper/2205.01780/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [A Variational Inequality Perspective on Generative Adversarial Networks](https://mlsanity.com/paper/1802.10551/) (2018)
- [openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit](https://mlsanity.com/paper/1605.06778/) (2016)
- [End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning](https://mlsanity.com/paper/1802.01115/) (2018)
- [Generating Diverse Realistic Laughter for Interactive Art](https://mlsanity.com/paper/2111.03146/) (2021)

## Built on by (most cited first, 16 of 16)

- [An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era](https://mlsanity.com/paper/2210.03538/) (2022)
- [The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression](https://mlsanity.com/paper/2207.03572/) (2022)
- [Exploring speaker enrolment for few-shot personalisation in emotional vocalisation prediction](https://mlsanity.com/paper/2206.06680/) (2022)
- [Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion, Age, and Origin from Vocal Bursts](https://mlsanity.com/paper/2206.12469/) (2022)
- [Generating Diverse Vocal Bursts with StyleGAN2 and MEL-Spectrograms](https://mlsanity.com/paper/2206.12563/) (2022)
- [Expressivity and Speech Synthesis](https://mlsanity.com/paper/2404.19363/) (2024)
- [Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression](https://mlsanity.com/paper/2206.11049/) (2022)
- [Self-supervision and Learnable STRFs for Age, Emotion, and Country Prediction](https://mlsanity.com/paper/2206.12568/) (2022)
- [Synthesizing Personalized Non-speech Vocalization from Discrete Speech Representations](https://mlsanity.com/paper/2206.12662/) (2022)
- [Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers](https://mlsanity.com/paper/2206.12494/) (2022)
- [Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts](https://mlsanity.com/paper/2209.07384/) (2022)
- [An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysis](https://mlsanity.com/paper/2209.13914/) (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 2026 ACII Dyadic Conversations (DaiKon) Workshop & Challenge](https://mlsanity.com/paper/2605.02672/) (2026)
- [The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data](https://mlsanity.com/paper/2403.14048/) (2024)

## Abstract (arXiv)

The ICML Expressive Vocalization (ExVo) Competition is focused on understanding and generating vocal bursts: laughs, gasps, cries, and other non-verbal vocalizations that are central to emotional expression and communication. ExVo 2022, includes three competition tracks using a large-scale dataset of 59,201 vocalizations from 1,702 speakers. The first, ExVo-MultiTask, requires participants to train a multi-task model to recognize expressed emotions and demographic traits from vocal bursts. The second, ExVo-Generate, requires participants to train a generative model that produces vocal bursts conveying ten different emotions. The third, ExVo-FewShot, requires participants to leverage few-shot learning incorporating speaker identity to train a model for the recognition of 10 emotions conveyed by vocal bursts. This paper describes the three tracks and provides performance measures for baseline models using state-of-the-art machine learning strategies. The baseline for each track is as follows, for ExVo-MultiTask, a combined score, computing the harmonic mean of Concordance Correlation Coefficient (CCC), Unweighted Average Recall (UAR), and inverted Mean Absolute Error (MAE) (SMTL) is at best, 0.335 SMTL; for ExVo-Generate, we report Fr'echet inception distance (FID) scores ranging from 4.81 to 8.27 (depending on the emotion) between the training set and generated samples. We then combine the inverted FID with perceptual ratings of the generated samples (SGen) and obtain 0.174 SGen; and for ExVo-FewShot, a mean CCC of 0.444 is obtained.