DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911757561233408 |
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| author | Liu, Alexander H. Chang, Heng-Jui Auli, Michael Hsu, Wei-Ning Glass, James R. |
| author_facet | Liu, Alexander H. Chang, Heng-Jui Auli, Michael Hsu, Wei-Ning Glass, James R. |
| contents | In this paper, we introduce self-distillation and online clustering for self-supervised speech representation learning (DinoSR) which combines masked language modeling, self-distillation, and online clustering. We show that these concepts complement each other and result in a strong representation learning model for speech. DinoSR first extracts contextualized embeddings from the input audio with a teacher network, then runs an online clustering system on the embeddings to yield a machine-discovered phone inventory, and finally uses the discretized tokens to guide a student network. We show that DinoSR surpasses previous state-of-the-art performance in several downstream tasks, and provide a detailed analysis of the model and the learned discrete units. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_10005 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning Liu, Alexander H. Chang, Heng-Jui Auli, Michael Hsu, Wei-Ning Glass, James R. Computation and Language In this paper, we introduce self-distillation and online clustering for self-supervised speech representation learning (DinoSR) which combines masked language modeling, self-distillation, and online clustering. We show that these concepts complement each other and result in a strong representation learning model for speech. DinoSR first extracts contextualized embeddings from the input audio with a teacher network, then runs an online clustering system on the embeddings to yield a machine-discovered phone inventory, and finally uses the discretized tokens to guide a student network. We show that DinoSR surpasses previous state-of-the-art performance in several downstream tasks, and provide a detailed analysis of the model and the learned discrete units. |
| title | DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2305.10005 |