# Comparing supervised and self-supervised embedding for ExVo Multi-Task learning track

arXiv: https://arxiv.org/abs/2206.11968 · HTML: https://mlsanity.com/paper/2206.11968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2022.

## Built on by (most cited first, 4 of 4)

- [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://mlsanity.com/paper/2211.04054/) (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)

## Abstract (arXiv)

The ICML Expressive Vocalizations (ExVo) Multi-task challenge 2022, focuses on understanding the emotional facets of the non-linguistic vocalizations (vocal bursts (VB)). The objective of this challenge is to predict emotional intensities for VB, being a multi-task challenge it also requires to predict speakers' age and native-country. For this challenge we study and compare two distinct embedding spaces namely, self-supervised learning (SSL) based embeddings and task-specific supervised learning based embeddings. Towards that, we investigate feature representations obtained from several pre-trained SSL neural networks and task-specific supervised classification neural networks. Our studies show that the best performance is obtained with a hybrid approach, where predictions derived via both SSL and task-specific supervised learning are used. Our best system on test-set surpasses the ComPARE baseline (harmonic mean of all sub-task scores i.e., SMTL) by a relative 13% margin.