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.
@article{arxiv.2206.11968,
title = {Comparing supervised and self-supervised embedding for ExVo Multi-Task learning track},
author = {Tilak Purohit and Imen Ben Mahmoud and Bogdan Vlasenko and Mathew Magimai. -Doss},
journal= {arXiv preprint arXiv:2206.11968},
year = {2022}
}