Papers with Equal Error Rate
MelTrim: Coarse-to-Fine Data Pruning for Speech Classification (2026.findings-acl)
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Shaobo Wang, Tianle Niu, Xuan Ouyang, Xintong Li, Zhengkun Ge, Yue Min, Xiaoqian Liu, Hankun Wang, Linfeng Zhang
| Challenge: | Unlike image or text classification, speech classification tasks are particularly challenging due to the difficulty in capturing the acoustic, semantic, and contextual representations. |
| Approach: | They propose a dataset pruning method that coarsely filters redundant samples using DBSCAN clustering on Mel-Frequency Cepstral Coefficients (MFCC) features. |
| Outcome: | The proposed method achieves 49.5% improvement in WA on the MEAD dataset and 41.9% reduction in EER on speaker identification tasks. |
A Unified Feature Mixture Framework for Joint Speech and Singing Deepfake Detection (2026.findings-acl)
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| Challenge: | Existing methods for deepfake detection fail under speech-to-singing domain shift . a speech-retentive multi-domain fine-tuning strategy enables adaptation to singing . |
| Approach: | They propose a unified deepfake detector based on a multi-branch mixture-of-experts architecture that integrates three complementary feature views. |
| Outcome: | The proposed detector achieves 1.82% EER on CtrSVDD, compared to 37–62% for existing detectors . it can generalize to unseen generators and preserve strong speech performance . |