@inproceedings{maltais-etal-2026-naijas2st,
title = "{N}aija{S}2{ST}: A Multi-Accent Benchmark for Speech-to-Speech Translation in Low-Resource {N}igerian Languages",
author = "Maltais, Marie and
Jeon, Yejin and
Ma, Min and
Muhammad, Shamsuddeen Hassan and
Abdulmumin, Idris and
Mukhtar, Maryam Ibrahim and
Abolade, Daud and
Monday, Okepefi Joel and
Okepefi, Johnson Sewedo and
Adelani, David Ifeoluwa",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.665/",
pages = "15311--15334",
ISBN = "979-8-89176-459-0",
abstract = "Speech translation for low-resource languages is constrained by the scarcity of high-quality parallel speech data, which is particularly pronounced in African linguistic contexts. To address this, we introduce , a parallel speech translation dataset covering Igbo, Hausa, , and Nigerian Pidgin paired with English. The dataset contains more than 40 hours of speech per language and includes several speaker and accent variations to reflect realistic settings. Using NaijaS2ST, we benchmark cascaded, end-to-end (E2E), and AudioLLM-based approaches across bidirectional translation directions (XX$\leftrightarrow$English). Our results show that few-shot audio LLMs are more effective than cascaded and E2E methods finetuned for speech-to-text translation. For speech-to-speech translation, however, cascaded and audio LLM paradigms perform comparatively, highlighting the need for stronger task-specific models. offers a strong foundation towards advancing research on low-resource multilingual speech translation. We open source the dataset here: [NaijaS2ST Huggingface Dataset](https://huggingface.co/datasets/McGill-NLP/NaijaS2ST); and the code here: [NaijaS2ST Code](https://github.com/McGill-NLP/naijas2st)."
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<abstract>Speech translation for low-resource languages is constrained by the scarcity of high-quality parallel speech data, which is particularly pronounced in African linguistic contexts. To address this, we introduce , a parallel speech translation dataset covering Igbo, Hausa, , and Nigerian Pidgin paired with English. The dataset contains more than 40 hours of speech per language and includes several speaker and accent variations to reflect realistic settings. Using NaijaS2ST, we benchmark cascaded, end-to-end (E2E), and AudioLLM-based approaches across bidirectional translation directions (XXłeftrightarrowEnglish). Our results show that few-shot audio LLMs are more effective than cascaded and E2E methods finetuned for speech-to-text translation. For speech-to-speech translation, however, cascaded and audio LLM paradigms perform comparatively, highlighting the need for stronger task-specific models. offers a strong foundation towards advancing research on low-resource multilingual speech translation. We open source the dataset here: [NaijaS2ST Huggingface Dataset](https://huggingface.co/datasets/McGill-NLP/NaijaS2ST); and the code here: [NaijaS2ST Code](https://github.com/McGill-NLP/naijas2st).</abstract>
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%0 Conference Proceedings
%T NaijaS2ST: A Multi-Accent Benchmark for Speech-to-Speech Translation in Low-Resource Nigerian Languages
%A Maltais, Marie
%A Jeon, Yejin
%A Ma, Min
%A Muhammad, Shamsuddeen Hassan
%A Abdulmumin, Idris
%A Mukhtar, Maryam Ibrahim
%A Abolade, Daud
%A Monday, Okepefi Joel
%A Okepefi, Johnson Sewedo
%A Adelani, David Ifeoluwa
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F maltais-etal-2026-naijas2st
%X Speech translation for low-resource languages is constrained by the scarcity of high-quality parallel speech data, which is particularly pronounced in African linguistic contexts. To address this, we introduce , a parallel speech translation dataset covering Igbo, Hausa, , and Nigerian Pidgin paired with English. The dataset contains more than 40 hours of speech per language and includes several speaker and accent variations to reflect realistic settings. Using NaijaS2ST, we benchmark cascaded, end-to-end (E2E), and AudioLLM-based approaches across bidirectional translation directions (XXłeftrightarrowEnglish). Our results show that few-shot audio LLMs are more effective than cascaded and E2E methods finetuned for speech-to-text translation. For speech-to-speech translation, however, cascaded and audio LLM paradigms perform comparatively, highlighting the need for stronger task-specific models. offers a strong foundation towards advancing research on low-resource multilingual speech translation. We open source the dataset here: [NaijaS2ST Huggingface Dataset](https://huggingface.co/datasets/McGill-NLP/NaijaS2ST); and the code here: [NaijaS2ST Code](https://github.com/McGill-NLP/naijas2st).
%U https://aclanthology.org/2026.emnlp-main.665/
%P 15311-15334
Markdown (Informal)
[NaijaS2ST: A Multi-Accent Benchmark for Speech-to-Speech Translation in Low-Resource Nigerian Languages](https://aclanthology.org/2026.emnlp-main.665/) (Maltais et al., EMNLP 2026)
ACL
- Marie Maltais, Yejin Jeon, Min Ma, Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Maryam Ibrahim Mukhtar, Daud Abolade, Okepefi Joel Monday, Johnson Sewedo Okepefi, and David Ifeoluwa Adelani. 2026. NaijaS2ST: A Multi-Accent Benchmark for Speech-to-Speech Translation in Low-Resource Nigerian Languages. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15311–15334, Budapest, Hungary. Association for Computational Linguistics.