3 papers
cs.CL2025
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…
eess.AS2024
Conditioning and Sampling in Variational Diffusion Models for Speech Super-Resolution
Chin-Yun Yu, Sung-Lin Yeh, György Fazekas +1
Recently, diffusion models (DMs) have been increasingly used in audio processing tasks, including speech super-resolution (SR), which aims to restore high-frequency content given l…
cs.CL2024
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…