1 citations · 2 across the 6 of their papers we have counts for
6 papers
A Large-Scale Evaluation of Speech Foundation Models
Shu-wen Yang, Heng-Jui Chang, Zili Huang +18
The foundation model paradigm leverages a shared foundation model to achieve state-of-the-art (SOTA) performance for various tasks, requiring minimal downstream-specific modeling a…
Investigating Human-Identifiable Features Hidden in Adversarial Perturbations
Dennis Y. Menn, Tzu-hsun Feng, Sriram Vishwanath +1
Neural networks perform exceedingly well across various machine learning tasks but are not immune to adversarial perturbations. This vulnerability has implications for real-world a…
Ensemble knowledge distillation of self-supervised speech models
Kuan-Po Huang, Tzu-hsun Feng, Yu-Kuan Fu +5
Distilled self-supervised models have shown competitive performance and efficiency in recent years. However, there is a lack of experience in jointly distilling multiple self-super…
Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech Transformers
Tzu-Quan Lin, Tsung-Huan Yang, Chun-Yao Chang +4
Transformer-based self-supervised models have achieved remarkable success in speech processing, but their large size and high inference cost present significant challenges for real…
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning
Tzu-hsun Feng, Annie Dong, Ching-Feng Yeh +11
We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency. The challenge buil…
Searching for the Essence of Adversarial Perturbations
Dennis Y. Menn, Tzu-hsun Feng, Hung-yi Lee
Neural networks have demonstrated state-of-the-art performance in various machine learning fields. However, the introduction of malicious perturbations in input data, known as adve…