13 papers
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…
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…
An Effective Strategy for Modeling Score Ordinality and Non-uniform Intervals in Automated Speaking Assessment
Tien-Hong Lo, Szu-Yu Chen, Yao-Ting Sung +1
A recent line of research on automated speaking assessment (ASA) has benefited from self-supervised learning (SSL) representations, which capture rich acoustic and linguistic patte…
Contextual Biasing for Streaming ASR via CTC-based Word Spotting
Kai-Chen Tsai, Tien-Hong Lo, Yun-Ting Sun +1
Contextual biasing is essential to improving the recognition of rare and domain-specific words in an automatic speech recognition (ASR) system. While numerous methods have been pro…
MALEFA: Multi-grAnularity Learning and Effective False Alarm Suppression for Zero-shot Keyword Spotting
Lo-Ya Li, Tien-Hong Lo, Jeih-Weih Hung +2
User-defined keyword spotting (KWS) without resorting to domain-specific pre-labeled training data is of fundamental importance in building adaptable and personalized voice interfa…
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…