Mamba matches Conformer accuracy for ASR in seven South African languages with lower compute, multilingual training improves results, and language embeddings aid cross-corpus robustness but do not capture typological similarity.
Swivuriso: The South African Next Voices Multilingual Speech Dataset
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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.
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A tone-conditioned curriculum framework with gated adapters achieves 28.41% average WER on six Southern Bantu languages, showing architecture-specific performance differences between W2V-BERT and Whisper.
citing papers explorer
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From Monolingual to Multilingual: Evaluating Mamba for ASR in South African Languages
Mamba matches Conformer accuracy for ASR in seven South African languages with lower compute, multilingual training improves results, and language embeddings aid cross-corpus robustness but do not capture typological similarity.
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Tone-Conditioned Curriculum Learning for Low-Resource Bantu Speech Recognition
A tone-conditioned curriculum framework with gated adapters achieves 28.41% average WER on six Southern Bantu languages, showing architecture-specific performance differences between W2V-BERT and Whisper.