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        <title>Evaluating Yoruba Text-to-Speech Systems for Accessible Computer-Based Testing in Visually Impaired Learners</title>
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    <abstract>Text-to-Speech (TTS) technology offers potential to improve exam accessibility for visually impaired learners, but existing systems often underperform in underrepresented languages like Yoruba. This study evaluates current Yoruba TTS models in delivering standardized exam content to five visually impaired students through a web-based interface. Before testing, four Yoruba TTS systems were compared; only Facebook’s mms-tts-yor and YarnGPT produced intelligible Yoruba speech. Students experienced exam questions delivered by human voice, Braille, and TTS. All preferred Braille for clarity and independence, some valued human narration, while TTS was least favored due to robotic and unclear output. These results reveal a significant gap between TTS capabilities and the needs of users in low-resource languages. The paper highlights the urgency of developing tone-aware, user-centered TTS solutions to ensure equitable access to digital education for visually impaired speakers of underrepresented languages.</abstract>
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%0 Conference Proceedings
%T Evaluating Yoruba Text-to-Speech Systems for Accessible Computer-Based Testing in Visually Impaired Learners
%A Moshood, Kausar Yetunde
%A Olufemi, Victor Tolulope
%A Babatunde, Oreoluwa Boluwatife
%A Bolarinwa, Emmanuel
%A Oluwademilade, Williams
%Y Chimoto, Everlyn Asiko
%Y Lignos, Constantine
%Y Muhammad, Shamsuddeen
%Y Abdulmumin, Idris
%Y Siro, Clemencia
%Y Adelani, David Ifeoluwa
%S Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-364-7
%F moshood-etal-2026-evaluating
%X Text-to-Speech (TTS) technology offers potential to improve exam accessibility for visually impaired learners, but existing systems often underperform in underrepresented languages like Yoruba. This study evaluates current Yoruba TTS models in delivering standardized exam content to five visually impaired students through a web-based interface. Before testing, four Yoruba TTS systems were compared; only Facebook’s mms-tts-yor and YarnGPT produced intelligible Yoruba speech. Students experienced exam questions delivered by human voice, Braille, and TTS. All preferred Braille for clarity and independence, some valued human narration, while TTS was least favored due to robotic and unclear output. These results reveal a significant gap between TTS capabilities and the needs of users in low-resource languages. The paper highlights the urgency of developing tone-aware, user-centered TTS solutions to ensure equitable access to digital education for visually impaired speakers of underrepresented languages.
%U https://aclanthology.org/2026.africanlp-main.23/
%P 228-234

Markdown (Informal)

[Evaluating Yoruba Text-to-Speech Systems for Accessible Computer-Based Testing in Visually Impaired Learners](https://aclanthology.org/2026.africanlp-main.23/) (Moshood et al., AfricaNLP 2026)

• Evaluating Yoruba Text-to-Speech Systems for Accessible Computer-Based Testing in Visually Impaired Learners (Moshood et al., AfricaNLP 2026)

ACL

• Kausar Yetunde Moshood, Victor Tolulope Olufemi, Oreoluwa Boluwatife Babatunde, Emmanuel Bolarinwa, and Williams Oluwademilade. 2026. Evaluating Yoruba Text-to-Speech Systems for Accessible Computer-Based Testing in Visually Impaired Learners. In Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026), pages 228–234, Rabat, Morocco. Association for Computational Linguistics.