How much speech data is necessary for ASR in African languages? An evaluation of data scaling in Kinyarwanda and Kikuyu

• URL: http://arxiv.org/abs/2510.07221v1
• Date: Wed, 08 Oct 2025 16:55:28 GMT
• Title: How much speech data is necessary for ASR in African languages? An evaluation of data scaling in Kinyarwanda and Kikuyu
• Authors: Benjamin Akera, Evelyn Nafula, Patrick Walukagga, Gilbert Yiga, John Quinn, Ernest Mwebaze,
• Abstract summary: Development of Automatic Speech Recognition systems for low-resource African languages remains challenging due to limited transcribed speech data.<n>Recent advances in large multilingual models like OpenAI's Whisper offer promising pathways for low-resource ASR development.<n>We evaluate Whisper's performance through comprehensive experiments on two Bantu languages.
• Score: 0.5678475267829229
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of Automatic Speech Recognition (ASR) systems for low-resource African languages remains challenging due to limited transcribed speech data. While recent advances in large multilingual models like OpenAI's Whisper offer promising pathways for low-resource ASR development, critical questions persist regarding practical deployment requirements. This paper addresses two fundamental concerns for practitioners: determining the minimum data volumes needed for viable performance and characterizing the primary failure modes that emerge in production systems. We evaluate Whisper's performance through comprehensive experiments on two Bantu languages: systematic data scaling analysis on Kinyarwanda using training sets from 1 to 1,400 hours, and detailed error characterization on Kikuyu using 270 hours of training data. Our scaling experiments demonstrate that practical ASR performance (WER < 13\%) becomes achievable with as little as 50 hours of training data, with substantial improvements continuing through 200 hours (WER < 10\%). Complementing these volume-focused findings, our error analysis reveals that data quality issues, particularly noisy ground truth transcriptions, account for 38.6\% of high-error cases, indicating that careful data curation is as critical as data volume for robust system performance. These results provide actionable benchmarks and deployment guidance for teams developing ASR systems across similar low-resource language contexts. We release accompanying and models see https://github.com/SunbirdAI/kinyarwanda-whisper-eval

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