Papers by Fu-An Chao

2 papers
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution (2024.findings-naacl)

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Challenge: Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the transcript of a learner’s speech.
Approach: They propose to use metric-based classification and loss re-weighting to model the impact of different SSL-based embedding features on the CEFR score.
Outcome: The proposed model outperforms baselines on the ICNALE benchmark dataset, achieving a significant improvement of more than 10% in CEFR prediction accuracy.
Towards Efficient and Multifaceted Computer-assisted Pronunciation Training Leveraging Hierarchical Selective State Space Model and Decoupled Cross-entropy Loss (2025.naacl-long)

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Challenge: APA and MDD are two of the main tasks of computer-assisted pronunciation training (CAPT) systems.
Approach: They propose a computer-assisted pronunciation training approach that integrates APA and MDD tasks in parallel.
Outcome: The proposed approach improves on APA and MDD tasks, and achieves an F1 score of 63.85%.

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