Summary of A Contrastive Learning Approach to Mitigate Bias in Speech Models, by Alkis Koudounas et al.
A Contrastive Learning Approach to Mitigate Bias in Speech Models
by Alkis Koudounas, Flavio Giobergia, Eliana Pastor, Elena Baralis
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); 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 method employs a three-level learning technique to mitigate speech model bias in underperforming subgroups. This approach guides the model to focus on different scopes for the contrastive loss, including task, subgroup, and errors within subgroups. The paper demonstrates its effectiveness on two spoken language understanding datasets and two languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tackles an important issue in speech recognition by proposing a method to reduce bias in underperforming subgroups. By using a three-level learning technique, the approach improves internal representations of these subgroups, leading to better performance and reduced bias. The results show that this method can be effective in real-world scenarios. |
Keywords
* Artificial intelligence * Contrastive loss * Language understanding
