7 citations · 18 across the 16 of their papers we have counts for
3 papers · 1 filter
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…
MelHuBERT: A simplified HuBERT on Mel spectrograms
Tzu-Quan Lin, Hung-yi Lee, Hao Tang
Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks. However, most self-supervised models require a lar…
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…