Papers with speaker verification systems
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. |
MirasVoice: A bilingual (English-Persian) speech corpus (L18-1)
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| Challenge: | Existing research and development areas in speech recognition are focused on the language of speakers. |
| Approach: | They propose to use a bilingual (English-Farsi) speech corpus to validate and explore speaker verification systems. |
| Outcome: | The proposed corpus can be used in a variety of language dependent and independent applications. |
SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions (2025.findings-emnlp)
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| Challenge: | Existing benchmarks only evaluate a subset of potential conditions, missing others entirely. |
| Approach: | a new benchmark suite evaluates speaker verification models under a variety of stressors . a san francisco-based team evaluates models under natural and background conditions . |
| Outcome: | a new benchmark suite evaluates speaker verification models under stressors under a variety of conditions . the results show that some models perform better under stress conditions than others . |