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

Abstract:Although modern multilingual Automatic Speech Recognition (ASR) systems support several Nigerian languages, their performance consistently lags behind resource-rich languages such as English and French. Nigerian languages present unique modelling hurdles, including acute data scarcity, inconsistent orthography, tonal diacritics, diverse accents, frequent code-switching, and localised named entities. To address these challenges, we developed a multilingual ASR framework using a two-stage distillation process. First, we employed student-teacher knowledge distillation from existing monolingual models, conditioned on robust language-specific N-gram language models. Second, we performed iterative self improvement using pseudo-labelled data to further refine accuracy. Our method significantly bridges the performance gap, achieving on average a reduction in the relative Word Error Rate (WER) of 29% over the monolingual baselines. Our models also outperform state-of-the-art multilingual models across major benchmarks, including Common Voice and FLEURS. We introduce Sometin Beta Pass Notin (SBPN), a multilingual foundational ASR model that covers Yorùbá, Hausa, Igbo, Nigerian Pidgin, and Nigerian English.

Accepted at Proc. SLT 2026, 7 pages

Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)

arXiv:2605.17710 [cs.CL]

(or arXiv:2605.17710v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.17710 Focus to learn more arXiv-issued DOI via DataCite

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