An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution
Tien-Hong Lo, Fu-An Chao, Tzu-i Wu, Yao-Ting Sung, Berlin Chen
Abstract
Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner’s speech. Recently, self-supervised learning (SSL) has shown stellar performance compared to traditional methods. However, SSL-based ASA systems are faced with at least three data-related challenges: limited annotated data, uneven distribution of learner proficiency levels and non-uniform score intervals between different CEFR proficiency levels. To address these challenges, we explore the use of two novel modeling strategies: metric-based classification and loss re-weighting, leveraging distinct SSL-based embedding features. Extensive experimental results on the ICNALE benchmark dataset suggest that our approach can outperform existing strong baselines by a sizable margin, achieving a significant improvement of more than 10% in CEFR prediction accuracy.
BibTeX
@inproceedings{lo-etal-2024-effective,
title = "An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution",
author = "Lo, Tien-Hong and
Chao, Fu-An and
Wu, Tzu-i and
Sung, Yao-Ting and
Chen, Berlin",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.86/",
doi = "10.18653/v1/2024.findings-naacl.86",
pages = "1352--1362"
}