title: Spectral Salt-and-Pepper Patch Masking for Self-Supervised Speech Representation Learning
abstract: Recent advanced systems in the speech recognition domain use large Transformer neural networks that have been pretrained on massive speech data. General methods in the deep learning area have been frequently shared across various domains, and the Transformer model can also be used effectively across speech and image. In this paper, we introduce a novel masking method for self-supervised speech representation learning with salt-and-pepper (S&amp;P) mask which is commonly used in computer vision. 
journal: Mathematics