7 papers
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…
SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding
Po-Yen Chen, Berlin Chen
Prompt-based spoken language understanding (SLU) with large language models (LLMs) often suffers from inconsistent intent--slot structures due to decoding stochasticity, particular…
Personalized Keyword Spotting for User-Defined Keywords Leveraging Text-Independent Speaker Verification
Ming-Hsiang Hu, Kuan-Tang Huang, Chien-Chun Wang +2
User-defined keyword spotting (UD-KWS) enables zero-shot wake-word detection from text, but existing systems learn speaker-invariant representations that cannot reject impostors ut…
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…