Comparing supervised and self-supervised embedding for ExVo Multi-Task learning track
| Type of publication: | Conference paper |
| Citation: | Purohit_ICML_2022 |
| Publication status: | Accepted |
| 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. |
| Keywords: | Emotion Recognition, Expressive Vocalizations, Multi-task learning, Self-supervised embedding |
| Projects |
EMIL |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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