Summary of Exploring Self-supervised Multi-view Contrastive Learning For Speech Emotion Recognition with Limited Annotations, by Bulat Khaertdinov et al.
Exploring Self-Supervised Multi-view Contrastive Learning for Speech Emotion Recognition with Limited Annotations
by Bulat Khaertdinov, Pedro Jeuris, Annanda Sousa, Enrique Hortal
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed multi-view SSL pre-training technique improves Speech Emotion Recognition (SER) performance in scenarios with limited annotations. By applying this approach to various speech representations, including those generated by large speech models, SER performance is boosted by up to 10% in Unweighted Average Recall. This advancement leverages recent progress in Deep and Self-Supervised Learning (SSL), achieving unprecedented levels of accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to improve Speech Emotion Recognition (SER). It’s hard to get enough labeled data for training, but this method uses large speech models and other speech representations to make SER better. The results show that it works well, even when there’s very little annotated data available. |
Keywords
» Artificial intelligence » Recall » Self supervised
