Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from <i>unlabeled</i> data. By designing <i>pretext tasks</i> that exploit statistical regularities, SSL models can capture <i>useful</i> representation...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/15/2/114 |
| _version_ | 1827343479848566784 |
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| author | Yusuf Brima Ulf Krumnack Simone Pika Gunther Heidemann |
| author_facet | Yusuf Brima Ulf Krumnack Simone Pika Gunther Heidemann |
| author_sort | Yusuf Brima |
| collection | DOAJ |
| description | Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from <i>unlabeled</i> data. By designing <i>pretext tasks</i> that exploit statistical regularities, SSL models can capture <i>useful</i> representations that are <i>transferable to downstream tasks</i>. Barlow Twins (BTs) is an SSL technique inspired by theories of redundancy reduction in human perception. In downstream tasks, BTs representations accelerate learning and transfer this learning across applications. This study applies BTs to speech data and evaluates the obtained representations on several downstream tasks, showing the applicability of the approach. However, limitations exist in disentangling key explanatory factors, with redundancy reduction and invariance alone being insufficient for factorization of learned latents into <i>modular</i>, <i>compact</i>, and <i>informative</i> codes. Our ablation study isolated gains from invariance constraints, but the gains were context-dependent. Overall, this work substantiates the potential of Barlow Twins for sample-efficient speech encoding. However, challenges remain in achieving fully hierarchical representations. The analysis methodology and insights presented in this paper pave a path for extensions incorporating further inductive priors and perceptual principles to further enhance the BTs self-supervision framework. |
| first_indexed | 2024-03-07T22:27:52Z |
| format | Article |
| id | doaj.art-aa7e2b91d7f14583a0a5a05ba9d425e0 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-07T22:27:52Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-aa7e2b91d7f14583a0a5a05ba9d425e02024-02-23T15:21:10ZengMDPI AGInformation2078-24892024-02-0115211410.3390/info15020114Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy ReductionYusuf Brima0Ulf Krumnack1Simone Pika2Gunther Heidemann3Computer Vision, Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrück, GermanyComputer Vision, Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrück, GermanyComparative BioCognition, Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrück, GermanyComputer Vision, Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrück, GermanySelf-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from <i>unlabeled</i> data. By designing <i>pretext tasks</i> that exploit statistical regularities, SSL models can capture <i>useful</i> representations that are <i>transferable to downstream tasks</i>. Barlow Twins (BTs) is an SSL technique inspired by theories of redundancy reduction in human perception. In downstream tasks, BTs representations accelerate learning and transfer this learning across applications. This study applies BTs to speech data and evaluates the obtained representations on several downstream tasks, showing the applicability of the approach. However, limitations exist in disentangling key explanatory factors, with redundancy reduction and invariance alone being insufficient for factorization of learned latents into <i>modular</i>, <i>compact</i>, and <i>informative</i> codes. Our ablation study isolated gains from invariance constraints, but the gains were context-dependent. Overall, this work substantiates the potential of Barlow Twins for sample-efficient speech encoding. However, challenges remain in achieving fully hierarchical representations. The analysis methodology and insights presented in this paper pave a path for extensions incorporating further inductive priors and perceptual principles to further enhance the BTs self-supervision framework.https://www.mdpi.com/2078-2489/15/2/114acoustic analysisBarlow Twinsself-supervised learninginvarianceredundancy reductionspeech representation learning |
| spellingShingle | Yusuf Brima Ulf Krumnack Simone Pika Gunther Heidemann Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction Information acoustic analysis Barlow Twins self-supervised learning invariance redundancy reduction speech representation learning |
| title | Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction |
| title_full | Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction |
| title_fullStr | Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction |
| title_full_unstemmed | Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction |
| title_short | Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction |
| title_sort | understanding self supervised learning of speech representation via invariance and redundancy reduction |
| topic | acoustic analysis Barlow Twins self-supervised learning invariance redundancy reduction speech representation learning |
| url | https://www.mdpi.com/2078-2489/15/2/114 |
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