# Synthetic Voice Data for Automatic Speech Recognition in African Languages

arXiv: https://arxiv.org/abs/2507.17578 · HTML: https://mlsanity.com/paper/2507.17578/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 61 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 30 of 38)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Will we run out of data? Limits of LLM scaling based on human-generated data](https://mlsanity.com/paper/2211.04325/) (2022)
- [Seamless: Multilingual Expressive and Streaming Speech Translation](https://mlsanity.com/paper/2312.05187/) (2023)
- [XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model](https://mlsanity.com/paper/2406.04904/) (2024)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse](https://mlsanity.com/paper/2404.05090/) (2024)
- [Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs](https://mlsanity.com/paper/2310.07251/) (2023)
- [On the Diversity of Synthetic Data and its Impact on Training Large Language Models](https://mlsanity.com/paper/2410.15226/) (2024)
- [Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias](https://mlsanity.com/paper/2403.07857/) (2024)
- [LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition](https://mlsanity.com/paper/2008.03687/) (2020)
- [PARIKSHA: A Large-Scale Investigation of Human-LLM Evaluator Agreement on Multilingual and Multi-Cultural Data](https://mlsanity.com/paper/2406.15053/) (2024)
- [Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese](https://mlsanity.com/paper/2402.17302/) (2024)
- [Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition](https://mlsanity.com/paper/2110.11479/) (2021)
- [Zambezi Voice: A Multilingual Speech Corpus for Zambian Languages](https://mlsanity.com/paper/2306.04428/) (2023)
- [Jawaher: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking](https://mlsanity.com/paper/2503.00231/) (2025)
- [CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages](https://mlsanity.com/paper/2306.10097/) (2023)
- [Correcting FLORES Evaluation Dataset for Four African Languages](https://mlsanity.com/paper/2409.00626/) (2024)
- [What is lost in Normalization? Exploring Pitfalls in Multilingual ASR Model Evaluations](https://mlsanity.com/paper/2409.02449/) (2024)
- [Bias Amplification: Large Language Models as Increasingly Biased Media](https://mlsanity.com/paper/2410.15234/) (2024)
- [Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap](https://mlsanity.com/paper/2410.16726/) (2024)
- [1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis](https://mlsanity.com/paper/2406.11727/) (2024)

## Built on by (most cited first, 3 of 3)

- [Cost Analysis of Human-corrected Transcription for Predominately Oral Languages](https://mlsanity.com/paper/2510.12781/) (2025)
- [Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara](https://mlsanity.com/paper/2512.19400/) (2025)
- [Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research](https://mlsanity.com/paper/2601.09716/) (2025)

## Abstract (arXiv)

Speech technology remains out of reach for most of the over 2300 languages in Africa. We present the first systematic assessment of large-scale synthetic voice corpora for African ASR. We apply a three-step process: LLM-driven text creation, TTS voice synthesis, and ASR fine-tuning. Eight out of ten languages for which we create synthetic text achieved readability scores above 5 out of 7. We evaluated ASR improvement for three (Hausa, Dholuo, Chichewa) and created more than 2,500 hours of synthetic voice data at below 1% of the cost of real data. Fine-tuned Wav2Vec-BERT-2.0 models trained on 250h real and 250h synthetic Hausa matched a 500h real-data-only baseline, while 579h real and 450h to 993h synthetic data created the best performance. We also present gender-disaggregated ASR performance evaluation. For very low-resource languages, gains varied: Chichewa WER improved about 6.5% relative with a 1:2 real-to-synthetic ratio; a 1:1 ratio for Dholuo showed similar improvements on some evaluation data, but not on others. Investigating intercoder reliability, ASR errors and evaluation datasets revealed the need for more robust reviewer protocols and more accurate evaluation data. All data and models are publicly released to invite further work to improve synthetic data for African languages.