Sometin Beta Pass Notin: Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation

The paper presents Sometin Beta Pass Notin (SBPN), a multilingual ASR framework for Nigerian languages that uses a two‑stage knowledge‑distillation approach. First, student‑teacher distillation from monolingual models is conditioned on language‑specific N‑gram language models; second, iterative self‑improvement with pseudo‑labelled data further refines accuracy. The method reduces relative WER by 29% over monolingual baselines and outperforms state‑of‑the‑art multilingual models on Common Voice and FLEURS benchmarks for Yoruba, Hausa, Igbo, Nigerian Pidgin, and Nigerian English.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

• llms
• efficiency
• multimodal
• benchmarks