mlsanityData to 14 Oct 2026

Building African Voices

arXiv 2207.00688 ↗First posted 1 Jul 2022cs.CL cs.SD eess.AScited by 14

Not on the map. Why?

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.

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

6 of 35 references.

References · 6

  1. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
  2. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
  3. 2020Participatory Research for Low-resourced Machine Translation: A Case Study in African Languagescited by 159
  4. 2021SynthASR: Unlocking Synthetic Data for Speech Recognitioncited by 34
  5. 2018Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesiscited by 30
  6. 2021RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesiscited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 4; 2025: 1; 2026: 1.

14 papers.

Sort: