An Empirical Analysis of Speech Self-Supervised Learning at Multiple Resolutions

• URL: http://arxiv.org/abs/2410.23955v1
• Date: Thu, 31 Oct 2024 14:09:05 GMT
• Title: An Empirical Analysis of Speech Self-Supervised Learning at Multiple Resolutions
• Authors: Theo Clark, Benedetta Cevoli, Eloy de Jong, Timofey Abramski, Jamie Dougherty,
• Abstract summary: We present an initial analysis of layer-wise representations in multi-scale architectures, with a focus on Canonical Correlation Analysis (CCA) and Mutual Information (MI) We find that the improved performance on SUPERB tasks is primarily due to the auxiliary low-resolution loss rather than the downsampling itself. These findings challenge assumptions about the multi-scale nature of MR-HuBERT and motivate the importance of disentangling computational efficiency from learning better representations.
• Score: 0.3495246564946556
• License:
• Abstract: Self-supervised learning (SSL) models have become crucial in speech processing, with recent advancements concentrating on developing architectures that capture representations across multiple timescales. The primary goal of these multi-scale architectures is to exploit the hierarchical nature of speech, where lower-resolution components aim to capture representations that align with increasingly abstract concepts (e.g., from phones to words to sentences). Although multi-scale approaches have demonstrated some improvements over single-scale models, the precise reasons for these enhancements have poor empirical support. In this study, we present an initial analysis of layer-wise representations in multi-scale architectures, with a focus on Canonical Correlation Analysis (CCA) and Mutual Information (MI). We apply this analysis to Multi-Resolution HuBERT (MR-HuBERT) and find that (1) the improved performance on SUPERB tasks is primarily due to the auxiliary low-resolution loss rather than the downsampling itself, and (2) downsampling to lower resolutions neither improves downstream performance nor correlates with higher-level information (e.g., words), though it does improve computational efficiency. These findings challenge assumptions about the multi-scale nature of MR-HuBERT and motivate the importance of disentangling computational efficiency from learning better representations.

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