Tone-Conditioned Curriculum Learning for Low-Resource Bantu Speech Recognition
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Computer Science > Computation and Language
Title:Tone-Conditioned Curriculum Learning for Low-Resource Bantu Speech Recognition
Abstract:Southern Bantu languages are spoken by over 80 million people, yet current foundation ASR models still produce zero-shot WER above 100%, which limits practical use in education and public services. We addressed this gap with a tone conditioned curriculum framework for 6 Southern Bantu languages that combined hybrid difficulty scoring, gated adapters driven by tonal statistics and staged curriculum training. We trained on a community corpus and tested transfer to NCHLT to measure robustness beyond matched evaluation. Results revealed clear interactions between architecture and language, with W2V-BERT outperforming Whisper on Nguni languages by 3 to 4 WER points whilst Whisper performed better on Sotho-Tswana languages. W2V-BERT with tone conditioning reached 28.41% average WER across datasets and 23.79% on Xitsonga transfer. No single model suited all 6 languages, so deployment should pair model selection per language with validation across corpora.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.31642 [cs.CL] |
| (or arXiv:2606.31642v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31642
arXiv-issued DOI via DataCite (pending registration)
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