CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Accesso online: | |
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| _version_ | 1866914721736687616 |
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| author | Budaghyan, David Onu, Charles C. Gorin, Arsenii Subakan, Cem Precup, Doina |
| author_facet | Budaghyan, David Onu, Charles C. Gorin, Arsenii Subakan, Cem Precup, Doina |
| contents | This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is a public speaker verification challenge based on cry sounds. We released more than 6 hours of manually segmented cry sounds from 786 newborns for academic use, aiming to encourage research in infant cry analysis. The inaugural public competition attracted 59 participants, 11 of whom improved the baseline performance. The top-performing system achieved a significant improvement scoring 25.8% equal error rate, which is still far from the performance of state-of-the-art adult speaker verification systems. Therefore, we believe there is room for further research on this dataset, potentially extending beyond the verification task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_00969 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds Budaghyan, David Onu, Charles C. Gorin, Arsenii Subakan, Cem Precup, Doina Sound Artificial Intelligence Computation and Language Audio and Speech Processing This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is a public speaker verification challenge based on cry sounds. We released more than 6 hours of manually segmented cry sounds from 786 newborns for academic use, aiming to encourage research in infant cry analysis. The inaugural public competition attracted 59 participants, 11 of whom improved the baseline performance. The top-performing system achieved a significant improvement scoring 25.8% equal error rate, which is still far from the performance of state-of-the-art adult speaker verification systems. Therefore, we believe there is room for further research on this dataset, potentially extending beyond the verification task. |
| title | CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds |
| topic | Sound Artificial Intelligence Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2305.00969 |