Curriculum learning for self-supervised speaker verification
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866916124184018944 |
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| author | Heo, Hee-Soo Jung, Jee-weon Kang, Jingu Kwon, Youngki Kim, You Jin Lee, Bong-Jin Chung, Joon Son |
| author_facet | Heo, Hee-Soo Jung, Jee-weon Kang, Jingu Kwon, Youngki Kim, You Jin Lee, Bong-Jin Chung, Joon Son |
| contents | The goal of this paper is to train effective self-supervised speaker representations without identity labels. We propose two curriculum learning strategies within a self-supervised learning framework. The first strategy aims to gradually increase the number of speakers in the training phase by enlarging the used portion of the train dataset. The second strategy applies various data augmentations to more utterances within a mini-batch as the training proceeds. A range of experiments conducted using the DINO self-supervised framework on the VoxCeleb1 evaluation protocol demonstrates the effectiveness of our proposed curriculum learning strategies. We report a competitive equal error rate of 4.47% with a single-phase training, and we also demonstrate that the performance further improves to 1.84% by fine-tuning on a small labelled dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_14525 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Curriculum learning for self-supervised speaker verification Heo, Hee-Soo Jung, Jee-weon Kang, Jingu Kwon, Youngki Kim, You Jin Lee, Bong-Jin Chung, Joon Son Audio and Speech Processing The goal of this paper is to train effective self-supervised speaker representations without identity labels. We propose two curriculum learning strategies within a self-supervised learning framework. The first strategy aims to gradually increase the number of speakers in the training phase by enlarging the used portion of the train dataset. The second strategy applies various data augmentations to more utterances within a mini-batch as the training proceeds. A range of experiments conducted using the DINO self-supervised framework on the VoxCeleb1 evaluation protocol demonstrates the effectiveness of our proposed curriculum learning strategies. We report a competitive equal error rate of 4.47% with a single-phase training, and we also demonstrate that the performance further improves to 1.84% by fine-tuning on a small labelled dataset. |
| title | Curriculum learning for self-supervised speaker verification |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2203.14525 |