Swivuriso: The South African Next Voices Multilingual Speech Dataset

2025-12-01

AI Summary

High-quality, multi-domain speech data for automatic speech recognition (ASR) in South Africa’s seven official languages is critically scarce, hindering robust ASR development for these low-resource languages. Method: We construct the first large-scale, multi-domain speech dataset covering all seven languages—totaling 3,000 hours—with content spanning agriculture, healthcare, and other domains. Data collection adheres to strict ethical guidelines; transcripts undergo rigorous human verification, and audio is processed via standardized preprocessing to ensure high fidelity and annotation accuracy. Contribution/Results: This dataset fills a critical gap in benchmark resources for indigenous low-resource ASR in South Africa, enabling end-to-end model training and cross-domain acoustic modeling. Extensive experiments across mainstream ASR architectures—including conformer, wav2vec 2.0, and Whisper—demonstrate that models trained or fine-tuned on our dataset consistently outperform those based on existing public benchmarks. The dataset provides a reproducible, scalable foundation for evaluation and development of ASR systems for under-resourced languages.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

This paper introduces Swivuriso, a 3000-hour multilingual speech dataset developed as part of the African Next Voices project, to support the development and benchmarking of automatic speech recognition (ASR) technologies in seven South African languages. Covering agriculture, healthcare, and general domain topics, Swivuriso addresses significant gaps in existing ASR datasets. We describe the design principles, ethical considerations, and data collection procedures that guided the dataset creation. We present baseline results of training/finetuning ASR models with this data and compare to other ASR datasets for the langauges concerned.

Problem

• Addresses gaps in multilingual ASR datasets for South African languages
• Supports ASR development and benchmarking across seven languages
• Covers agriculture, healthcare, and general domain topics

Innovation

• Multilingual speech dataset for seven South African languages
• Covers agriculture, healthcare, and general domain topics
• Provides baseline ASR model training and benchmarking results