Overview
The WavLM model was proposed in WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen,
Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu,
Michael Zeng, Furu Wei.
The abstract from the paper is the following:
Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been
attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker
identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is
challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks.
