MEG-GLOUPS a curated dataset of raw magneto-encephalography (MEG) recordings from French speakers completing a pseudo-word learning task, along with resting-state recordings before and after the task. The seventeen participants pronounced visually and auditorily presented pseudo-words that followed or violated French phonotactic rules. The dataset adheres to the Brain Imaging Data Structure (BIDS) standard and includes basic preprocessing and quality checks. Comprehensive documentation covers the study’s rationale, design, data collection, and validation.

Valérie Chanoine, Snežana Todorović, Bruno Nazarian, Jean-Michel Badier, Khoubeib Kanzari, Andrea Brovelli, Sonja A. Kotz, and Elin Runnqvist.
Dataset for Evaluating the Production of Phonotactically Legal and Illegal Pseudowords
2025. Scientific Data 12 (1): 792. —  @HAL

MarmAudio is a database of common marmoset vocalisations, recorded from an animal facility that houses around 20 marmosets in three cages. The dataset comprises more than 800,000 files of a few seconds each, amounting to 253 hours of data. These recordings capture the marmosets’ social vocalisations, encompassing their entire known vocal repertoire. The vocalisations were projected into a 16-dimensional auto-encoder latent space then visualised in 2D using UMAP — Uniform Manifold Approximation and Projection. Vocalisation types (e.g., trill, twitter) are indicated by colour.

Charly Lamothe, Manon Obliger-Debouche, Paul Best, Régis Trapeau, Sabrina Ravel, Thierry Artières, Ricard Marxer, and Pascal Belin.
A Large Annotated Dataset of Vocalizations by Common Marmosets
2025. Scientific Data 12 (1): 782. —  @HAL

We explored knowledge transfer capabilities of pre-trained speech models with vocalizations from the closest living relatives of humans: non-human primates. We assessed model performance in identifying individual gibbons based on their songs using linear probing. When compared to models pre-trained on bird songs or general audio, speech-based models appear to produce rich bioacoustic representations, encoding vocal content information over background noise and effectively capturing the temporal dynamics of gibbon songs.

Jules Cauzinille, Benoît Favre, Ricard Marxer, Dena Clink, Abdul Hamid Ahmad, and Arnaud Rey.
Investigating Self-Supervised Speech Models’ Ability to Classify Animal Vocalizations: The Case of Gibbon’s Vocal Signatures
In Interspeech 2024, 132–36. ISCA. —  @HAL