Can Text-to-Speech Systems enable Inclusive Computer-Based Testing? An Evaluation of Yoruba TTS for Visually Impaired Learners

Kausar Yetunde Moshood

Keywords: Text-to-Speech (TTS), Computer-Based Testing (CBT), Visually Impaired Learners, Accessibility in Education, Tonal Languages, Underrepresented Languages in AI.

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.

Submission Number: 135