activity
20162024
most citedWavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

1.9k citations · 2.8k across the 43 of their papers we have counts for

collaborators
Showing eess.ASShow all

12 papers · 1 filter

eess.AS2023★ 3 cited

On decoder-only architecture for speech-to-text and large language model integration

Jian Wu, Yashesh Gaur, Zhuo Chen +8

Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. Howeve…

eess.AS2022★ 43 cited

BEATs: Audio Pre-Training with Acoustic Tokenizers

Sanyuan Chen, Yu Wu, Chengyi Wang +4

The massive growth of self-supervised learning (SSL) has been witnessed in language, vision, speech, and audio domains over the past few years. While discrete label prediction is w…

eess.AS2022

Speech separation with large-scale self-supervised learning

Zhuo Chen, Naoyuki Kanda, Jian Wu +6

Self-supervised learning (SSL) methods such as WavLM have shown promising speech separation (SS) results in small-scale simulation-based experiments. In this work, we extend the ex…

eess.AS2022

Exploring WavLM on Speech Enhancement

Hyungchan Song, Sanyuan Chen, Zhuo Chen +5

There is a surge in interest in self-supervised learning approaches for end-to-end speech encoding in recent years as they have achieved great success. Especially, WavLM showed sta…

eess.AS2022★ 15 cited

Ultra Fast Speech Separation Model with Teacher Student Learning

Sanyuan Chen, Yu Wu, Zhuo Chen +5

Transformer has been successfully applied to speech separation recently with its strong long-dependency modeling capacity using a self-attention mechanism. However, Transformer ten…

eess.AS2021★ 2 cited

Self-Supervised Learning for speech recognition with Intermediate layer supervision

Chengyi Wang, Yu Wu, Sanyuan Chen +4

Recently, pioneer work finds that speech pre-trained models can solve full-stack speech processing tasks, because the model utilizes bottom layers to learn speaker-related informat…