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@INPROCEEDINGS{Purohit_ICML_2022,
                      author = {Purohit, Tilak and Ben Mahmoud, Imen and Vlasenko, Bogdan and Magimai-Doss, Mathew},
                    keywords = {Emotion Recognition, Expressive Vocalizations, Multi-task learning, Self-supervised embedding},
                    projects = {EMIL},
                       title = {Comparing supervised and self-supervised embedding for ExVo Multi-Task learning track},
                   booktitle = {Proceedings of the ICML Expressive Vocalizations Workshop held in conjunction with the 39th International Conference on Machine Learning},
                        year = {2022},
                    location = {Maryland, USA},
                    abstract = {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 an hybrid approach, where predictions derived via both SSL and task-specific supervised learning are used. Our best system on test-set surpass the ComPARE baseline (harmonic-mean of all sub-task scores i.e., S_MLT) by a relative 13\% margin.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2022/Purohit_ICML_2022.pdf}
}