H
Hsuan-Jui Chen
Publications - 5
Citations - 58
Hsuan-Jui Chen is an academic researcher. The author has contributed to research in topics: Engineering & Computer science. The author has an hindex of 3, co-authored 5 publications receiving 58 citations.
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Proceedings ArticleDOI
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities
Hsiang-Sheng Tsai,Heng-Jui Chang,Wen-Chin Huang,Zili Huang,Kushal Lakhotia,Shu-wen Yang,Shuyan Dong,Andy Liu,Cheng-I Lai,Jiatong Shi,Xuankai Chang,Phil Hall,Hsuan-Jui Chen,Shang-Wen Li,Shinji Watanabe,Abdelrahman Mohamed,Hung-yi Lee +16 more
TL;DR: This paper introduces SUPERB-SG, a new benchmark focusing on evaluating the semantic and generative capabilities of pre- trained models by increasing task diversity and difficulty over SUPERB, and uses a lightweight methodology to test the robustness of representations learned by pre-trained models under shifts in data domain and quality across different types of tasks.
Proceedings ArticleDOI
On Compressing Sequences for Self-Supervised Speech Models
TL;DR: This work studiesed-length and variable-length subsampling along the time axis in self-supervised learning and explores how individual downstream tasks are sensitive to input frame rates.
Proceedings ArticleDOI
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering
Guan-Ting Lin,Yung-Sung Chuang,Ho-Lam Chung,Shu-wen Yang,Hsuan-Jui Chen,Shuyan Dong,Shang-Wen Li,Abdelrahman Mohamed,Hung-yi Lee,Lin-Shan Lee +9 more
TL;DR: Discrete Spoken Unit Adaptive Learning (DUAL) is proposed, leveraging unlabeled data for pre-training and beingtuned by the SQA downstream task, which empirically showed yields results comparable to those obtained by cascading ASR and text QA model and robust to real-world data.
DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning
Guan-Ting Lin,Yung-Sung Chuang,Ho-Lam Chung,Shu-wen Yang,Hsuan-Jui Chen,Shang-Wen Li,Abdelrahman Mohamed,Hung-yi Lee,Lin-Shan Lee +8 more
TL;DR: Results show that DUAL performs competi- 017 tively with the cascade approach (ASR + text 018 QA), and DUAL is robust to real-world speech.
Proceedings ArticleDOI
Once-for-All Sequence Compression for Self-Supervised Speech Models
TL;DR: This paper proposed a once-for-all (OFA) sequence compression framework for self-supervised speech models that supports a continuous range of operating compressing rates and showed marginal degradation compared to the fixed compressing rate variants with a smooth performance-efficiency trade-off.