6 papers
LOPA: Enhancing Spoken Language Assessment via Latent Ordinal Prototype Alignment
Hong-Yun Lin, Fu-An Chao, Bi-Cheng Yan +1
Fueled by increasing model scale and multimodal inputs, Multimodal Large Language Models (MLLMs) have emerged as a promising paradigm for Spoken Language Assessment (SLA). While ef…
The NTNU System at the S&I Challenge 2025 SLA Open Track
Hong-Yun Lin, Tien-Hong Lo, Yu-Hsuan Fang +4
A recent line of research on spoken language assessment (SLA) employs neural models such as BERT and wav2vec 2.0 (W2V) to evaluate speaking proficiency across linguistic and acoust…
Session-Level Spoken Language Assessment with a Multimodal Foundation Model via Multi-Target Learning
Hong-Yun Lin, Jhen-Ke Lin, Chung-Chun Wang +2
Spoken Language Assessment (SLA) estimates a learner's oral proficiency from spontaneous speech. The growing population of L2 English speakers has intensified the demand for reliab…
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions
Chung-Chun Wang, Jhen-Ke Lin, Hao-Chien Lu +2
Automated speaking assessment (ASA) on opinion expressions is often hampered by the scarcity of labeled recordings, which restricts prompt diversity and undermines scoring reliabil…
Acoustically Precise Hesitation Tagging Is Essential for End-to-End Verbatim Transcription Systems
Jhen-Ke Lin, Hao-Chien Lu, Chung-Chun Wang +2
Verbatim transcription for automatic speaking assessment demands accurate capture of disfluencies, crucial for downstream tasks like error analysis and feedback. However, many ASR…
Advancing Automated Speaking Assessment Leveraging Multifaceted Relevance and Grammar Information
Hao-Chien Lu, Jhen-Ke Lin, Hong-Yun Lin +2
Current automated speaking assessment (ASA) systems for use in multi-aspect evaluations often fail to make full use of content relevance, overlooking image or exemplar cues, and em…