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Self-supervised speech representation models have succeeded in various tasks,but improving them for content-related problems using unlabeled data ischallenging. We propose speaker-invariant clustering (Spin), a novelself-supervised learning method that clusters speech representations andperforms swapped prediction between the original and speaker-perturbedutterances. Spin disentangles speaker information and preserves contentrepresentations with just 45 minutes of fine-tuning on a single GPU. Spinimproves pre-trained networks and outperforms prior methods in speechrecognition and acoustic unit discovery.

Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering