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

Reexploring drivers of technological variation through the complex landscapes of cultural evolution.

James Clark

L. Timbrell

S. E. Paris

Gonzalo J. Linares-Matás

2026-04-16

Journal of anthropological sciences = Rivista di antropologia : JASS (publié)

doi.org

A Universal Systematic Method to Generate Error Patterns on Memoryless Channels

Marwan Jalaleddine

Jiajie Li

Syed Mohsin Abbas

Warren J. Gross

The high computational cost of approaching the performance of Maximum-likelihood (ML) decoding has limited its practical use for decades. Be… (voir plus)cause the complexity grows exponentially with the message length, researchers have spent years developing algorithms like Ordered Statistics Decoding (OSD), Partial Ordered Statistics Decoding (POSD) and Guessing Random Additive Noise decoding (GRAND) which try to approach ML performance. OSD, POSD and GRAND work by trying to guess the error patterns affecting the received signals. However, there does not exist a systematic method to extend the error pattern guesses to novel channels. This work introduces a systematic method that uses the Probability Density Function (PDF) of a memoryless channel to generate a set of error patterns that can be applied on any future received signal on this channel. Simulation results show that our proposed method applied on GRAND, OSD and POSD generally matches or outperforms current pre-generated error patterns on additive white Gaussian noise (AWGN) channel, mixture of Gaussian distribution channels, Rayleigh fading channel with perfect knowledge of Channel State Information (CSI) and Rayleigh fading channel with no perfect knowledge of Channel State Information (NCSI).

2026-04-16

arXiv (prépublication)

doi.org

arxiv.org

Why Open Source? A Game-Theoretic Analysis of the AI Race

Andjela Mladenovic

Aaron Courville

Gauthier Gidel

In recent years, with the advancement of frontier AI, we have observed certain dynamics in open-sourcing and closed-sourcing decisions. We p… (voir plus)ropose a game-theoretic model to analyze these dynamics in the current landscape of the AI race. Our model builds on an R&D race framework under a winner-takes-all setting, and it accounts for the cases where the players' actions can be either discrete or continuous (i.e., partial open-sourcing, such as open weights). We show that determining the existence of a discrete pure non-trivial Nash equilibrium is NP-hard in general but that we can transform the discrete Nash existence computation into a MIP (Mixed-Integer Programming) problem, making it tractable for small instances using a standard MIP solver. Next, we show the existence and tractability of pure Nash equilibria in the continuous version of our problem, leveraging standard convex analysis results, and constructing an equivalent MIP formulation. Throughout this work, we leverage both our main technical results as well as surrounding technical analysis, to derive socially relevant insights that we believe can serve both to understand already existing decisions and dynamics and to potentially inform new policies.

2026-04-16

arXiv (prépublication)

doi.org

arxiv.org

The Golden Rule of Big Memory: Persistence Is Not Harmful

Yu Hua

Xue Liu

Ion Stoica

Seeking a transformative memory scheme that grows in performance and capacity as the infrastructure expands.

2026-04-15

Communications of the ACM (publié)

doi.org

Neurovascular Coupling as Early, High-Sensitive Biomarker for Cognitive Decline and Vascular Pathology: Protocol for Systematic Review and Meta-Analysis

V. D. Abramova

Veronika Egovtseva

Ksenya Pronyaeva

Shamsa H. Alshamsi

Marta Estrada

Rustam Talybov

Taleb~M. Almansoori

Bassem Sadek

Mohammed Khogali

Mohammad~I.K. Hamad

Milos Ljubisavljevic

Danilo Bzdok

Yauhen Statsenko

2026-04-14

Research Square (accepté)

doi.org

RobotPan: A 360$^\circ$ Surround-View Robotic Vision System for Embodied Perception

Jiahao Ma

Qiang Zhang

Peiran Liu

Zeran Su

Pihai Sun

Gang Han

Wen Zhao

Wei Cui

Zhang Zhang

Zhiyuan Xu

Renjing Xu

Jian Tang

Miaomiao Liu

Yijie Guo

Surround-view perception is increasingly important for robotic navigation and loco-manipulation, especially in human-in-the-loop settings su… (voir plus)ch as teleoperation, data collection, and emergency takeover. However, current robotic visual interfaces are often limited to narrow forward-facing views, or, when multiple on-board cameras are available, require cumbersome manual switching that interrupts the operator's workflow. Both configurations suffer from motion-induced jitter that causes simulator sickness in head-mounted displays. We introduce a surround-view robotic vision system that combines six cameras with LiDAR to provide full 360

2026-04-14

arXiv (prépublication)

doi.org

arxiv.org

AI Methods for Implementation Science (AIM-IS): developing a framework, toolkit, and reporting standard for the responsible use of AI in implementation practice and research

Guillaume Fontaine

Susan Michie

Rinad S. Beidas

Elvin Geng

Christine Fahim

Byron J. Powell

Vivian Welch

James Thomas

J. Chan

Samira Abbasgholizadeh-Rahimi

France Légaré

Janna Hastings

Sylvie D. Lambert

Justin Presseau

Sharon E. Straus

Ruopeng An

Ashrita Saran

Natalie Taylor

Open Science Framework, March 15, 2026: https://doi.org/10.17605/OSF.IO/BX35K.

2026-04-13

Implementation Science (publié)

doi.org

Translating Brain Encoding Models to Clinical Cohorts: Challenges of Domain Adaptation

Lune Bellec

Marie St‐Laurent

Julie Boyle

Basile Pinsard

Elizabeth DuPré

We’ve optimized fMRI biomarkers to generalize across participants, not cognitive states. Brain encoding models might finally let us model … (voir plus)both and change what “good data” means. These are the slides of a presentation by Dr Lune Bellec at the AI4health workshop, ÉTS, Montréal, Feb 2026.

2026-04-13

Zenodo (inconnu)

doi.org

Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions

Manuela González‐González

Soufiane Belharbi

Muhammad Zeeshan

Masoumeh Sharafi

Muhammad Haseeb Aslam

Lorenzo Sia

Nicolas Richet

Marco Pedersoli

Alessandro L. Koerich

Simon L Bacon

Eric Granger

Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain hea… (voir plus)lthy habits that improve medical outcomes. In-person interventions are costly and difficult to scale, especially in resource-limited regions. Digital health interventions offer a cost-effective approach, potentially supporting independent living and self-management. Automating such interventions, especially through machine learning, has gained considerable attention recently. Ambivalence and hesitancy (A/H) play a primary role for individuals to delay, avoid, or abandon health interventions. A/H are subtle and conflicting emotions that place a person in a state between positive and negative evaluations of a behaviour, or between acceptance and refusal to engage in it. They manifest as affective inconsistency across modalities or within a modality, such as language, facial, vocal expressions, and body language. While experts can be trained to recognize A/H, integrating them into digital health interventions is costly and less effective. Automatic A/H recognition is therefore critical for the personalization and cost-effectiveness of digital health interventions. Here, we explore the application of deep learning models for A/H recognition in videos, a multi-modal task by nature. In particular, this paper covers three learning setups: supervised learning, unsupervised domain adaptation for personalization, and zero-shot inference via large language models (LLMs). Our experiments are conducted on the unique and recently published BAH video dataset for A/H recognition. Our results show limited performance, suggesting that more adapted multi-modal models are required for accurate A/H recognition. Better methods for modeling spatio-temporal and multimodal fusion are necessary to leverage conflicts within/across modalities.

2026-04-12

arXiv (prépublication)

doi.org

arxiv.org

DASB - Discrete Audio and Speech Benchmark

Pooneh Mousavi

Jarod Duret

Darius Petermann

Anastasia Kuznetsova

Discrete audio tokens have recently gained considerable attention for their potential to bridge audio and language processing, enabling mult… (voir plus)imodal language models that can both generate and understand audio. However, preserving key information such as phonetic content, speaker identity, and paralinguistic cues remains a major challenge. Identifying the optimal tokenizer and configuration is further complicated by inconsistent evaluation settings across existing studies. To address this, we introduce the Discrete Audio and Speech Benchmark (DASB), a comprehensive framework for benchmarking discrete audio tokens across speech, general audio, and music domains on a range of discriminative and generative tasks. Our results show that discrete representations are less robust than continuous ones and require careful tuning of factors such as model architecture, data size, learning rate, and capacity. Semantic tokens generally outperform acoustic tokens, but a gap remains between discrete tokens and continuous features, highlighting the need for further research. DASB codes, evaluation setup, and leaderboards are publicly available at https://poonehmousavi.github.io/DASB-website/.

2026-04-12

Transactions on Machine Learning Research (accepté)

doi.org

openreview.net

Early detection of common reed ( <i>Phragmites australis</i> ) using unoccupied aerial vehicles and deep learning

Antoine Caron-Guay

Mickaël Germain

Étienne Laliberté

2026-04-12

Canadian Journal of Remote Sensing (publié)

doi.org