Codec-SUPERB: An In-Depth Analysis of Sound Codec Models

1 code implementation • 20 Feb 2024 • Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin, Yuan-Kuei Wu, Xuanjun Chen, Yu-Chi Pai, Hsiu-Hsuan Wang, Kai-Wei Chang, Alexander H. Liu, Hung-Yi Lee

The sound codec's dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance.

Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond

no code implementations • 9 Oct 2023 • Jiatong Shi, William Chen, Dan Berrebbi, Hsiu-Hsuan Wang, Wei-Ping Huang, En-Pei Hu, Ho-Lam Chuang, Xuankai Chang, Yuxun Tang, Shang-Wen Li, Abdelrahman Mohamed, Hung-Yi Lee, Shinji Watanabe

The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification.

Language Identification speech-recognition +1

Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study

no code implementations • 27 Sep 2023 • Xuankai Chang, Brian Yan, Kwanghee Choi, Jeeweon Jung, Yichen Lu, Soumi Maiti, Roshan Sharma, Jiatong Shi, Jinchuan Tian, Shinji Watanabe, Yuya Fujita, Takashi Maekaku, Pengcheng Guo, Yao-Fei Cheng, Pavel Denisov, Kohei Saijo, Hsiu-Hsuan Wang

Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling.

Automatic Speech Recognition Self-Supervised Learning +3

CLCIFAR: CIFAR-Derived Benchmark Datasets with Human Annotated Complementary Labels

1 code implementation • 15 May 2023 • Hsiu-Hsuan Wang, Wei-I Lin, Hsuan-Tien Lin

Through extensive benchmark experiments, we discovered a notable decline in performance when transitioning from synthetic datasets to real-world datasets.

Weakly-supervised Learning