Portrait de Marie Maltais

Marie Maltais

Doctorat - McGill
Superviseur⋅e principal⋅e
David Ifeoluwa Adelani
Sujets de recherche
Apprentissage à quelques exemples
Apprentissage automatique appliqué
Apprentissage automatique pour la parole et l'audio
Apprentissage par transfert
Éthique de l'IA
Évaluation linguistique des modèles de langage
IA centrée sur l'humain
IA et culture
IA pour l'humanité
IA responsable
Modèle de langage
Traduction automatique
Traitement de la parole
Traitement du langage naturel
GitHub

Publications

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
Speech translation for low-resource languages remains fundamentally limited by the scarcity of high-quality, diverse parallel speech data, a… (voir plus) 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.
2026-04-16
arXiv (prépublication)
doi.org
arxiv.org