5 citations · 21 across the 24 of their papers we have counts for
10 papers · 1 filter
T-SANDHI: Tone Sandhi-aware Adaptive Network with Decoupled Hybrid Injection for Low-resource Taiwanese Hokkien Speech Recognition
Hung-Yang Sung, Chien-Chun Wang, Tien-Hong Lo +3
In Taiwanese Hokkien automatic speech recognition (ASR), prior studies often treat tone sandhi as a major challenge under the assumption that models fail to process implicit phonol…
COALA: Robust Contextualized Speech-augmented Language Modeling for ASR via Contrastive Regularizer and Biasing Score Estimation
Jhih-Rong Guo, Bi-Cheng Yan, Tien-Hong Lo +1
Contextual biasing seeks to integrate external knowledge into automatic speech recognition (ASR) systems to accurately recognize domain-specific entities. In this paper, we propose…
Efficient Dialect-Aware Modeling and Conditioning for Low-Resource Taiwanese Hakka Speech Processing
An-Ci Peng, Kuan-Tang Huang, Tien-Hong Lo +3
Taiwanese Hakka is a low-resource, endangered language that poses significant challenges for automatic speech recognition (ASR), including high dialectal variability and the presen…
CLiFT-ASR: A Cross-Lingual Fine-Tuning Framework for Low-Resource Taiwanese Hokkien Speech Recognition
Hung-Yang Sung, Chien-Chun Wang, Kuan-Tang Huang +4
Automatic speech recognition (ASR) for low-resource languages such as Taiwanese Hokkien is difficult due to the scarcity of annotated data. However, direct fine-tuning on Han-chara…
Mitigating Data Imbalance in Automated Speaking Assessment
Fong-Chun Tsai, Kuan-Tang Huang, Bi-Cheng Yan +2
Automated Speaking Assessment (ASA) plays a crucial role in evaluating second-language (L2) learners proficiency. However, ASA models often suffer from class imbalance, leading to…
Beyond Modality Limitations: A Unified MLLM Approach to Automated Speaking Assessment with Effective Curriculum Learning
Yu-Hsuan Fang, Tien-Hong Lo, Yao-Ting Sung +1
Traditional Automated Speaking Assessment (ASA) systems exhibit inherent modality limitations: text-based approaches lack acoustic information while audio-based methods miss semant…
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…
Automated Speaking Assessment of Conversation Tests with Novel Graph-based Modeling on Spoken Response Coherence
Jiun-Ting Li, Bi-Cheng Yan, Tien-Hong Lo +3
Automated speaking assessment in conversation tests (ASAC) aims to evaluate the overall speaking proficiency of an L2 (second-language) speaker in a setting where an interlocutor i…
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition
Shih-Hsuan Chiu, Tien-Hong Lo, Fu-An Chao +1
How to effectively incorporate cross-utterance information cues into a neural language model (LM) has emerged as one of the intriguing issues for automatic speech recognition (ASR)…
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features
Shi-Yan Weng, Tien-Hong Lo, Berlin Chen
Tremendous amounts of multimedia associated with speech information are driving an urgent need to develop efficient and effective automatic summarization methods. To this end, we h…