CEC: A Noisy Label Detection Method for Speaker Recognition
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917699396829184 |
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| author | Shen, Yao Gao, Yingying Hao, Yaqian Hu, Chenguang Zhang, Fulin Feng, Junlan Zhang, Shilei |
| author_facet | Shen, Yao Gao, Yingying Hao, Yaqian Hu, Chenguang Zhang, Fulin Feng, Junlan Zhang, Shilei |
| contents | Noisy labels are inevitable, even in well-annotated datasets. The detection of noisy labels is of significant importance to enhance the robustness of speaker recognition models. In this paper, we propose a novel noisy label detection approach based on two new statistical metrics: Continuous Inconsistent Counting (CIC) and Total Inconsistent Counting (TIC). These metrics are calculated through Cross-Epoch Counting (CEC) and correspond to the early and late stages of training, respectively. Additionally, we categorize samples based on their prediction results into three categories: inconsistent samples, hard samples, and easy samples. During training, we gradually increase the difficulty of hard samples to update model parameters, preventing noisy labels from being overfitted. Compared to contrastive schemes, our approach not only achieves the best performance in speaker verification but also excels in noisy label detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13268 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CEC: A Noisy Label Detection Method for Speaker Recognition Shen, Yao Gao, Yingying Hao, Yaqian Hu, Chenguang Zhang, Fulin Feng, Junlan Zhang, Shilei Audio and Speech Processing Sound Noisy labels are inevitable, even in well-annotated datasets. The detection of noisy labels is of significant importance to enhance the robustness of speaker recognition models. In this paper, we propose a novel noisy label detection approach based on two new statistical metrics: Continuous Inconsistent Counting (CIC) and Total Inconsistent Counting (TIC). These metrics are calculated through Cross-Epoch Counting (CEC) and correspond to the early and late stages of training, respectively. Additionally, we categorize samples based on their prediction results into three categories: inconsistent samples, hard samples, and easy samples. During training, we gradually increase the difficulty of hard samples to update model parameters, preventing noisy labels from being overfitted. Compared to contrastive schemes, our approach not only achieves the best performance in speaker verification but also excels in noisy label detection. |
| title | CEC: A Noisy Label Detection Method for Speaker Recognition |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2406.13268 |