  This paper introduces Robust Spin (R-Spin), a data-efficient domain-specific
self-supervision method for speaker and noise-invariant speech representations
by learning discrete acoustic units with speaker-invariant clustering (Spin).
R-Spin resolves Spin's issues and enhances content representations by learning
to predict acoustic pieces. R-Spin offers a 12X reduction in computational
resources compared to previous state-of-the-art methods while outperforming
them in severely distorted speech scenarios. This paper provides detailed
analyses to show how discrete units contribute to speech encoder training and
improving robustness in diverse acoustic environments.
