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

• URL: http://arxiv.org/abs/2305.11072v1
• Date: Thu, 18 May 2023 15:59:36 GMT
• Title: Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering
• Authors: Heng-Jui Chang, Alexander H. Liu, James Glass
• Abstract summary: We propose speaker-invariant clustering (Spin) as a novel self-supervised learning method. Spin disentangles speaker information and preserves content representations with just 45 minutes of fine-tuning on a single GPU.
• Score: 78.2927924732142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised speech representation models have succeeded in various tasks, but improving them for content-related problems using unlabeled data is challenging. We propose speaker-invariant clustering (Spin), a novel self-supervised learning method that clusters speech representations and performs swapped prediction between the original and speaker-perturbed utterances. Spin disentangles speaker information and preserves content representations with just 45 minutes of fine-tuning on a single GPU. Spin improves pre-trained networks and outperforms prior methods in speech recognition and acoustic unit discovery.

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