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

Heng-Jui Chang; Alexander H. Liu; James Glass

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arXiv · 2305.11072

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

Heng-Jui Chang·Alexander H. Liu·James Glass

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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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Heng-Jui Chang, Alexander H. Liu, James Glass. 2023-05-18. Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering. https://arxiv.org/abs/2305.11072

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