Papers by Divya Sharma
FAtNet: Cost-Effective Approach Towards Mitigating the Linguistic Bias in Speaker Verification Systems (2022.findings-naacl)
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| Challenge: | Linguistic bias in Deep Neural Network (DNN) based systems is a critical challenge that needs attention. |
| Approach: | They propose to integrate a lightweight embedding with existing NLP systems to mitigate linguistic bias without adaptation. |
| Outcome: | The proposed framework reduces linguistic bias and enhances usability of baselines for twelve languages. |
EcoSpeak: Cost-Efficient Bias Mitigation for Partially Cross-Lingual Speaker Verification (2024.findings-naacl)
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| Challenge: | Linguistic bias is a critical problem that harms the diversity, equity, and inclusiveness of Natural Language Processing tools. |
| Approach: | They propose a low-cost solution that incorporates contrastive linguistic attention to emphasize relevant speaker verification embedding parts. |
| Outcome: | The proposed model is able to mitigate linguistic bias in five partially cross-lingual scenarios. |