An Effective Strategy for Modeling Score Ordinality and Non-uniform Intervals in Automated Speaking Assessment

• URL: http://arxiv.org/abs/2509.03372v2
• Date: Sun, 21 Sep 2025 05:40:47 GMT
• Title: An Effective Strategy for Modeling Score Ordinality and Non-uniform Intervals in Automated Speaking Assessment
• Authors: Tien-Hong Lo, Szu-Yu Chen, Yao-Ting Sung, Berlin Chen,
• Abstract summary: Self-supervised learning representations capture rich acoustic and linguistic patterns in non-native speech without underlying assumptions of feature curation.<n>Most prior arts treat proficiency levels as nominal classes, ignoring their ordinal structure and non-uniform intervals between proficiency labels.<n>We propose an effective ASA approach combining SSL with handcrafted indicator features via a novel modeling paradigm.
• Score: 14.003981407136072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A recent line of research on automated speaking assessment (ASA) has benefited from self-supervised learning (SSL) representations, which capture rich acoustic and linguistic patterns in non-native speech without underlying assumptions of feature curation. However, speech-based SSL models capture acoustic-related traits but overlook linguistic content, while text-based SSL models rely on ASR output and fail to encode prosodic nuances. Moreover, most prior arts treat proficiency levels as nominal classes, ignoring their ordinal structure and non-uniform intervals between proficiency labels. To address these limitations, we propose an effective ASA approach combining SSL with handcrafted indicator features via a novel modeling paradigm. We further introduce a multi-margin ordinal loss that jointly models both the score ordinality and non-uniform intervals of proficiency labels. Extensive experiments on the TEEMI corpus show that our method consistently outperforms strong baselines and generalizes well to unseen prompts.

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