pith:QOWGFSZF
Sometin Beta Pass Notin (SBPN): Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation
Knowledge distillation from monolingual models followed by self-improvement on pseudo labels improves multilingual ASR for Nigerian languages.
arxiv:2605.17710 v1 · 2026-05-18 · cs.CL · eess.AS
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Claims
Our method significantly bridges the performance gap, achieving on average a relative Word Error Rate (WER) reduction of 29 % over monolingual baselines. Our models also outperform state-of-the-art multilingual models across major benchmarks, including Common Voice and Fleurs.
The iterative self-improvement stage assumes that pseudo-labels generated by the initial distilled model are sufficiently accurate and do not introduce systematic errors that compound over iterations; this premise is invoked when describing the second stage of refinement on pseudo-labelled data.
SBPN is a multilingual ASR model for five Nigerian languages that delivers a 29% relative WER reduction over monolingual baselines through teacher-student distillation plus iterative pseudo-label self-improvement.
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| First computed | 2026-05-20T00:04:54.103176Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
83ac62cb2537b28dfd226e4140c58326bb3060503e6a6722d3407674b9ea0527
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/QOWGFSZFG6ZI37JCNZAUBRMDE2 \
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Canonical record JSON
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