1 code implementation • 15 Apr 2024 • Shu-wen Yang, Heng-Jui Chang, Zili Huang, Andy T. Liu, Cheng-I Lai, Haibin Wu, Jiatong Shi, Xuankai Chang, Hsiang-Sheng Tsai, Wen-Chin Huang, Tzu-hsun Feng, Po-Han Chi, Yist Y. Lin, Yung-Sung Chuang, Tzu-Hsien Huang, Wei-Cheng Tseng, Kushal Lakhotia, Shang-Wen Li, Abdelrahman Mohamed, Shinji Watanabe, Hung-Yi Lee
In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech.
1 code implementation • ACL 2022 • Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang, Zili Huang, Kushal Lakhotia, Shu-wen Yang, Shuyan Dong, Andy T. Liu, Cheng-I Jeff Lai, Jiatong Shi, Xuankai Chang, Phil Hall, Hsuan-Jui Chen, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-Yi Lee
In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB.
1 code implementation • 3 Mar 2022 • Andy T. Liu, Wei Xiao, Henghui Zhu, Dejiao Zhang, Shang-Wen Li, Andrew Arnold
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency.
no code implementations • 15 Oct 2021 • Yen Meng, Yi-Hui Chou, Andy T. Liu, Hung-Yi Lee
Self-supervised Speech Models (S3Ms) have been proven successful in many speech downstream tasks, like ASR.
no code implementations • 1 Jun 2021 • Haibin Wu, Xu Li, Andy T. Liu, Zhiyong Wu, Helen Meng, Hung-Yi Lee
This work is among the first to perform adversarial defense for ASV without knowing the specific attack algorithms.
5 code implementations • 3 May 2021 • Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y. Lin, Andy T. Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, Tzu-Hsien Huang, Wei-Cheng Tseng, Ko-tik Lee, Da-Rong Liu, Zili Huang, Shuyan Dong, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-Yi Lee
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
no code implementations • 14 Feb 2021 • Haibin Wu, Xu Li, Andy T. Liu, Zhiyong Wu, Helen Meng, Hung-Yi Lee
Automatic speaker verification (ASV) is one of the core technologies in biometric identification.
6 code implementations • 12 Jul 2020 • Andy T. Liu, Shang-Wen Li, Hung-Yi Lee
We present a large-scale comparison of various self-supervised models.
2 code implementations • 5 Jun 2020 • Shu-wen Yang, Andy T. Liu, Hung-Yi Lee
Self-supervised Audio Transformers (SAT) enable great success in many downstream speech applications like ASR, but how they work has not been widely explored yet.
5 code implementations • 5 Jun 2020 • Haibin Wu, Andy T. Liu, Hung-Yi Lee
To explore this issue, we proposed to employ Mockingjay, a self-supervised learning based model, to protect anti-spoofing models against adversarial attacks in the black-box scenario.
no code implementations • 5 Dec 2019 • Po-chun Hsu, Chun-hsuan Wang, Andy T. Liu, Hung-Yi Lee
We found out that the speaker variety is much more important for achieving a universal vocoder than the language.
7 code implementations • 25 Oct 2019 • Andy T. Liu, Shu-wen Yang, Po-Han Chi, Po-chun Hsu, Hung-Yi Lee
We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech.
1 code implementation • 28 May 2019 • Andy T. Liu, Po-chun Hsu, Hung-Yi Lee
We found that the proposed encoding method offers automatic extraction of speech content from speaker style, and is sufficient to cover full linguistic content in a given language.