Mitigating Data Imbalance in Automated Speaking Assessment
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908778009460736 |
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| author | Tsai, Fong-Chun Huang, Kuan-Tang Yan, Bi-Cheng Lo, Tien-Hong Chen, Berlin |
| author_facet | Tsai, Fong-Chun Huang, Kuan-Tang Yan, Bi-Cheng Lo, Tien-Hong Chen, Berlin |
| contents | Automated Speaking Assessment (ASA) plays a crucial role in evaluating second-language (L2) learners proficiency. However, ASA models often suffer from class imbalance, leading to biased predictions. To address this, we introduce a novel objective for training ASA models, dubbed the Balancing Logit Variation (BLV) loss, which perturbs model predictions to improve feature representation for minority classes without modifying the dataset. Evaluations on the ICNALE benchmark dataset show that integrating the BLV loss into a celebrated text-based (BERT) model significantly enhances classification accuracy and fairness, making automated speech evaluation more robust for diverse learners. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03010 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mitigating Data Imbalance in Automated Speaking Assessment Tsai, Fong-Chun Huang, Kuan-Tang Yan, Bi-Cheng Lo, Tien-Hong Chen, Berlin Computation and Language Machine Learning Audio and Speech Processing Automated Speaking Assessment (ASA) plays a crucial role in evaluating second-language (L2) learners proficiency. However, ASA models often suffer from class imbalance, leading to biased predictions. To address this, we introduce a novel objective for training ASA models, dubbed the Balancing Logit Variation (BLV) loss, which perturbs model predictions to improve feature representation for minority classes without modifying the dataset. Evaluations on the ICNALE benchmark dataset show that integrating the BLV loss into a celebrated text-based (BERT) model significantly enhances classification accuracy and fairness, making automated speech evaluation more robust for diverse learners. |
| title | Mitigating Data Imbalance in Automated Speaking Assessment |
| topic | Computation and Language Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.03010 |