# Building African Voices

arXiv: https://arxiv.org/abs/2207.00688 · HTML: https://mlsanity.com/paper/2207.00688/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [SynthASR: Unlocking Synthetic Data for Speech Recognition](https://mlsanity.com/paper/2106.07803/) (2021)
- [Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis](https://mlsanity.com/paper/1808.10128/) (2018)
- [RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis](https://mlsanity.com/paper/2106.08468/) (2021)

## Built on by (most cited first, 14 of 14)

- [AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR](https://mlsanity.com/paper/2310.00274/) (2023)
- [Improving Massively Multilingual ASR With Auxiliary CTC Objectives](https://mlsanity.com/paper/2302.12829/) (2023)
- [MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages](https://mlsanity.com/paper/2305.13989/) (2023)
- [Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech](https://mlsanity.com/paper/2210.15447/) (2022)
- [Meta Learning Text-to-Speech Synthesis in over 7000 Languages](https://mlsanity.com/paper/2406.06403/) (2024)
- [1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis](https://mlsanity.com/paper/2406.11727/) (2024)
- [When Is TTS Augmentation Through a Pivot Language Useful?](https://mlsanity.com/paper/2207.09889/) (2022)
- [YFACC: A Yor`ub'a speech-image dataset for cross-lingual keyword localisation through visual grounding](https://mlsanity.com/paper/2210.04600/) (2022)
- [AfriNames: Most ASR models "butcher" African Names](https://mlsanity.com/paper/2306.00253/) (2023)
- [Cultural Fidelity in Large-Language Models: An Evaluation of Online Language Resources as a Driver of Model Performance in Value Representation](https://mlsanity.com/paper/2410.10489/) (2024)
- [Visually Grounded Keyword Detection and Localisation for Low-Resource Languages](https://mlsanity.com/paper/2302.00765/) (2023)
- [Towards Digital Preservation of Efik: TTS for a Low-Resource African Language](https://mlsanity.com/paper/2607.04515/) (2026)
- [Improved Visually Prompted Keyword Localisation in Real Low-Resource Settings](https://mlsanity.com/paper/2409.06013/) (2024)
- [Kinship in Speech: Leveraging Linguistic Relatedness for Zero-Shot TTS in Indian Languages](https://mlsanity.com/paper/2506.03884/) (2025)

## Abstract (arXiv)

Modern speech synthesis techniques can produce natural-sounding speech given sufficient high-quality data and compute resources. However, such data is not readily available for many languages. This paper focuses on speech synthesis for low-resourced African languages, from corpus creation to sharing and deploying the Text-to-Speech (TTS) systems. We first create a set of general-purpose instructions on building speech synthesis systems with minimum technological resources and subject-matter expertise. Next, we create new datasets and curate datasets from "found" data (existing recordings) through a participatory approach while considering accessibility, quality, and breadth. We demonstrate that we can develop synthesizers that generate intelligible speech with 25 minutes of created speech, even when recorded in suboptimal environments. Finally, we release the speech data, code, and trained voices for 12 African languages to support researchers and developers.