Portrait of Marie Maltais

Marie Maltais

marie.maltais@mila.quebec
PhD - McGill University
Supervisor
David Ifeoluwa Adelani
Research Topics
AI and Culture
AI Ethics
AI4Humanity
Applied Machine Learning
Few-Shot Learning
Human-Centered AI
Language Model
Linguistic Evaluation of Language Models
Machine Learning For Speech and Audio
Machine Translation
Natural Language Processing
Responsible AI
Speech Processing
Transfer Learning
GitHub

Publications

VoxSumm: A Multilingual Corpus of Long-Form Spoken News for Joint Summarization and Translation
Yejin Jeon
Marie Maltais
Virginia Ceccatelli
Min Ma
David Ifeoluwa Adelani
As information increasingly traverses linguistic boundaries, users require concise cross-lingual representations of long-form content. Never… (see more)theless, long-document summarization research remains text-centric, whereas multilingual speech research has largely prioritized translation, preserving source content rather than compressing it. We address this methodological gap by formalizing joint speech summarization and translation (JSumT): the generation of a succinct, faithful target-language summary directly from a long spoken document in a source language. We additionally introduce VoxSumm, the first multilingual and cross-lingual benchmark for this task, comprising 10,045 BBC article-summary pairs across 24 languages and encompassing approximately 703 hours of speech data. Our evaluation of representative speech-language models reveals pronounced variation across models and generation settings: Gemini3.1-Pro demonstrates the greatest consistency, summarization into English generally surpasses generation into non-English target languages, and translating an entire document before summarization compounds instruction-following failures. Through the release of VoxSumm, we establish a foundation for developing and evaluating multilingual systems capable of jointly interpreting, compressing, and translating long-form speech.
2026-08-10
arXiv (preprint)
doi.org
arxiv.org
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… (see more) 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 (preprint)
doi.org
arxiv.org