WAXAL: A large-scale open resource for African language speech technology

WAXAL is an open speech dataset designed to address the severe lack of technology resources for African languages. Covering 27 Sub-Saharan African languages spoken by more than 100 million people, it offers 1,846 hours of transcribed speech for ASR and over 565 hours of high-quality recordings for TTS under the permissive CC-BY-4.0 license. Built with African academic and community organizations, WAXAL aims to support inclusive speech systems while preserving and expanding access to the continent’s linguistic diversity.

The Need for African-Language Speech Data

• Voice assistants and transcription tools primarily support high-resource languages.
• Hundreds of millions of African speakers remain underserved, despite Sub-Saharan Africa having more than 2,000 languages.
• Limited training data makes it difficult to build accurate systems for tonal, morphologically complex, and code-switching languages.
• WAXAL was developed beginning in 2021 to provide openly accessible resources for research and commercial innovation.

WAXAL-ASR: Natural, Spontaneous Speech

• Contains approximately 1,846 hours of transcribed audio.
• Participants described image prompts across more than 50 topics rather than reading prepared scripts.
• The method captured:
  • Natural linguistic variation
  • Tonal distinctions
  • Spontaneous speech patterns
  • Code-switching
• Image-based elicitation produced more realistic speech data than conventional scripted recordings.

WAXAL-TTS: High-Quality Voice Recordings

• Includes more than 565 hours of phonetically balanced, high-fidelity audio.
• Community members collaborated in pairs to create scripts of 10,000–20,000 words and alternate between reading and recording.
• Some participants built custom studio boxes to improve recording quality.
• Audio was segmented, aligned with transcripts, and reviewed for accuracy.
• The clean recordings support natural-sounding synthetic voices and conversational systems.

Supporting Full-Duplex Conversational Systems

• The ASR data represents varied, real-world speech input.
• The TTS data provides high-quality material for generating clear and natural speech.
• Together, the datasets can support systems that both understand and respond using African languages.

Community-Led Development

• African universities and community organizations led the data collection, with guidance from Google Research.
• Makerere University contributed data for nine languages.
• The University of Ghana worked on eight languages, including image-prompted ASR collection.
• Digital Umuganda and Addis Ababa University supported ASR collection for regional languages.
• Media Trust, Loud n Clear, and the African Institute for Mathematical Sciences Senegal led studio-quality TTS recordings.
• Partners retain ownership of the data while making it openly available under CC-BY-4.0.

Research Enabled by the Project

• A community-driven impaired-speech collection cookbook produced an open Akan dataset covering conditions such as cerebral palsy and stammering.
• A separate project introduced a 5,000-hour corpus for five Ghanaian languages: Akan, Ewe, Dagbani, Dagaare, and Ikposo.
• Benchmarking of Whisper, XLS-R, MMS, and W2v-BERT across 13 African languages showed that scaling benefits depend on linguistic complexity and domain alignment.
• A literature review cataloged 74 datasets spanning 111 African languages and emphasized the need for conversational, multi-domain data and metrics such as Character Error Rate (CER).

WAXAL provides a strong foundation for building more accurate and inclusive African-language speech technologies. Researchers and developers should use the openly licensed corpus, contribute derivative work, and help extend the collection to additional languages and communities.