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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.02201 |
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| _version_ | 1866914262530654208 |
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| author | Marivate, Vukosi Olaleye, Kayode Mundia, Sitwala Bakainga, Andinda Netshifhefhe, Unarine Milanzie, Mahmooda Mogale, Tsholofelo Hope Sindane, Thapelo Abdulrasaq, Zainab Mokgosi, Kesego Okorie, Chijioke Van Wyk, Nia Zion Morrissey, Graham Dunbar, Dale Smit, Francois Chidi, Tsosheletso Mabuya, Rooweither Bukula, Andiswa Mlambo, Respect Macucwa, Tebogo Abdulmumin, Idris Rananga, and Seani |
| author_facet | Marivate, Vukosi Olaleye, Kayode Mundia, Sitwala Bakainga, Andinda Netshifhefhe, Unarine Milanzie, Mahmooda Mogale, Tsholofelo Hope Sindane, Thapelo Abdulrasaq, Zainab Mokgosi, Kesego Okorie, Chijioke Van Wyk, Nia Zion Morrissey, Graham Dunbar, Dale Smit, Francois Chidi, Tsosheletso Mabuya, Rooweither Bukula, Andiswa Mlambo, Respect Macucwa, Tebogo Abdulmumin, Idris Rananga, and Seani |
| contents | This paper introduces Swivuriso, a 3000-hour multilingual speech dataset developed as part of the African Next Voices project, to support the development and benchmarking of automatic speech recognition (ASR) technologies in seven South African languages. Covering agriculture, healthcare, and general domain topics, Swivuriso addresses significant gaps in existing ASR datasets. We describe the design principles, ethical considerations, and data collection procedures that guided the dataset creation. We present baseline results of training/finetuning ASR models with this data and compare to other ASR datasets for the langauges concerned. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02201 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Swivuriso: The South African Next Voices Multilingual Speech Dataset Marivate, Vukosi Olaleye, Kayode Mundia, Sitwala Bakainga, Andinda Netshifhefhe, Unarine Milanzie, Mahmooda Mogale, Tsholofelo Hope Sindane, Thapelo Abdulrasaq, Zainab Mokgosi, Kesego Okorie, Chijioke Van Wyk, Nia Zion Morrissey, Graham Dunbar, Dale Smit, Francois Chidi, Tsosheletso Mabuya, Rooweither Bukula, Andiswa Mlambo, Respect Macucwa, Tebogo Abdulmumin, Idris Rananga, and Seani Computation and Language This paper introduces Swivuriso, a 3000-hour multilingual speech dataset developed as part of the African Next Voices project, to support the development and benchmarking of automatic speech recognition (ASR) technologies in seven South African languages. Covering agriculture, healthcare, and general domain topics, Swivuriso addresses significant gaps in existing ASR datasets. We describe the design principles, ethical considerations, and data collection procedures that guided the dataset creation. We present baseline results of training/finetuning ASR models with this data and compare to other ASR datasets for the langauges concerned. |
| title | Swivuriso: The South African Next Voices Multilingual Speech Dataset |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.02201 |