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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/2505.20564 |
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| _version_ | 1866911052431622144 |
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| author | Emezue, Chris Community, NaijaVoices Awobade, Busayo Owodunni, Abraham Emezue, Handel Emezue, Gloria Monica Tobechukwu Emezue, Nefertiti Nneoma Ogun, Sewade Akinremi, Bunmi Adelani, David Ifeoluwa Pal, Chris |
| author_facet | Emezue, Chris Community, NaijaVoices Awobade, Busayo Owodunni, Abraham Emezue, Handel Emezue, Gloria Monica Tobechukwu Emezue, Nefertiti Nneoma Ogun, Sewade Akinremi, Bunmi Adelani, David Ifeoluwa Pal, Chris |
| contents | The development of high-performing, robust, and reliable speech technologies depends on large, high-quality datasets. However, African languages -- including our focus, Igbo, Hausa, and Yoruba -- remain under-represented due to insufficient data. Popular voice-enabled technologies do not support any of the 2000+ African languages, limiting accessibility for circa one billion people. While previous dataset efforts exist for the target languages, they lack the scale and diversity needed for robust speech models. To bridge this gap, we introduce the NaijaVoices dataset, a 1,800-hour speech-text dataset with 5,000+ speakers. We outline our unique data collection approach, analyze its acoustic diversity, and demonstrate its impact through finetuning experiments on automatic speech recognition, averagely achieving 75.86% (Whisper), 52.06% (MMS), and 42.33% (XLSR) WER improvements. These results highlight NaijaVoices' potential to advance multilingual speech processing for African languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20564 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages Emezue, Chris Community, NaijaVoices Awobade, Busayo Owodunni, Abraham Emezue, Handel Emezue, Gloria Monica Tobechukwu Emezue, Nefertiti Nneoma Ogun, Sewade Akinremi, Bunmi Adelani, David Ifeoluwa Pal, Chris Computation and Language The development of high-performing, robust, and reliable speech technologies depends on large, high-quality datasets. However, African languages -- including our focus, Igbo, Hausa, and Yoruba -- remain under-represented due to insufficient data. Popular voice-enabled technologies do not support any of the 2000+ African languages, limiting accessibility for circa one billion people. While previous dataset efforts exist for the target languages, they lack the scale and diversity needed for robust speech models. To bridge this gap, we introduce the NaijaVoices dataset, a 1,800-hour speech-text dataset with 5,000+ speakers. We outline our unique data collection approach, analyze its acoustic diversity, and demonstrate its impact through finetuning experiments on automatic speech recognition, averagely achieving 75.86% (Whisper), 52.06% (MMS), and 42.33% (XLSR) WER improvements. These results highlight NaijaVoices' potential to advance multilingual speech processing for African languages. |
| title | The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.20564 |