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arXiv · 2604.16287

NaijaS2ST: A Multi-Accent Benchmark for Speech-to-Speech Translation in Low-Resource Nigerian Languages

Marie Maltais·Yejin Jeon·Min Ma·Shamsuddeen Hassan Muhammad·Idris Abdulmumin·Maryam Ibrahim Mukhtar·Daud Abolade·Joel Okepefi·Johnson Sewedo·David Ifeoluwa Adelani
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Abstract

Speech translation for low-resource languages remains fundamentally limited by the scarcity of high-quality, diverse parallel speech data, a challenge that is especially pronounced in African linguistic contexts. To address this, we introduce NaijaS2ST, a parallel speech translation dataset spanning Igbo, Hausa, Yor\`ub\'a, and Nigerian Pidgin paired with English. The dataset comprises approximately 50 hours of speech per language and captures substantial variation in speakers and accents, reflecting realistic multilingual and multi-accent conditions. With NaijaS2ST, we conduct a comprehensive benchmark of cascaded, end-to-end (E2E), and AudioLLM-based approaches across bidirectional translation settings. Our results show that audio LLMs with few-shot examples are more effective for speech-to-text translation than cascaded and end-to-end methods trained on fine-tuned data. However, for speech-to-speech translation, the cascaded and audio LLM paradigms yield comparable performance, indicating that there is still considerable room for improvement in developing targeted, task-specific models for this setting. By providing both a high-quality dataset and a systematic benchmark, we hope that NaijaS2ST will serve as a strong foundation for advancing research in low-resource, multilingual speech translation.

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Marie Maltais, Yejin Jeon, Min Ma, Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Maryam Ibrahim Mukhtar, Daud Abolade, Joel Okepefi, Johnson Sewedo, David Ifeoluwa Adelani. 2026-04-17. NaijaS2ST: A Multi-Accent Benchmark for Speech-to-Speech Translation in Low-Resource Nigerian Languages. https://arxiv.org/abs/2604.16287

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