46 citations · 177 across the 28 of their papers we have counts for
9 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…
Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models
Sung-Lin Yeh, Hao Tang
While discrete latent variable models have had great success in self-supervised learning, most models assume that frames are independent. Due to the segmental nature of phonemes in…
On Compressing Sequences for Self-Supervised Speech Models
Yen Meng, Hsuan-Jui Chen, Jiatong Shi +4
Compressing self-supervised models has become increasingly necessary, as self-supervised models become larger. While previous approaches have primarily focused on compressing the m…
Analyzing Acoustic Word Embeddings from Pre-trained Self-supervised Speech Models
Ramon Sanabria, Hao Tang, Sharon Goldwater
Given the strong results of self-supervised models on various tasks, there have been surprisingly few studies exploring self-supervised representations for acoustic word embeddings…
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…