@misc{luthra2026spidradaptuniversalspeechrepresentation,title={{SpidR}-{Adapt}: A Universal Speech Representation Model for Few-Shot Adaptation},author={Luthra, Mahi and Shen, Jiayi and Poli, Maxime and {Ortiz Tandazo}, Angelo and Higuchi, Yosuke and Benchekroun, Youssef and Gleize, Martin and Saint-James, Charles-Eric and Lin, Dongyan and Rust, Phillip and Villar, Angel and Parimi, Surya and Stark, Vanessa and Moritz, Rashel and Pino, Juan and LeCun, Yann and Dupoux, Emmanuel},year={2026},archiveprefix={arXiv},primaryclass={cs.CL},url={https://arxiv.org/abs/2512.21204},}
DiscoPhon: Benchmarking the Unsupervised Discovery of Phoneme Inventories With Discrete Speech Units
Maxime Poli, Manel Khentout, Angelo Ortiz Tandazo, Ewan Dunbar, and 2 more authors
@misc{poli2026discophonbenchmarkingunsuperviseddiscovery,title={DiscoPhon: Benchmarking the Unsupervised Discovery of Phoneme Inventories With Discrete Speech Units},author={Poli, Maxime and Khentout, Manel and {Ortiz Tandazo}, Angelo and Dunbar, Ewan and Chemla, Emmanuel and Dupoux, Emmanuel},year={2026},archiveprefix={arXiv},primaryclass={cs.CL},url={https://arxiv.org/abs/2603.18612},}
2025
MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery
Angelo Ortiz Tandazo, Manel Khentout, Youssef Benchekroun, Thomas Hueber, and 1 more author
@misc{ortiztandazo25_maubert,title={{MauBERT}: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery},author={{Ortiz Tandazo}, Angelo and Khentout, Manel and Benchekroun, Youssef and Hueber, Thomas and Dupoux, Emmanuel},year={2025},archiveprefix={arXiv},primaryclass={cs.CL},url={https://arxiv.org/abs/2512.19612},}
2024
Simulating articulatory trajectories with phonological feature interpolation
As a first step towards a complete computational model of speech learning involving perception-production loops, we investigate the forward mapping between pseudo-motor commands and articulatory trajectories. Two phonological feature sets, based respectively on generative and articulatory phonology, are used to encode a phonetic target sequence. Different interpolation techniques are compared to generate smooth trajectories in these feature spaces, with a potential optimisation of the target value and timing to capture co-articulation effects. We report the Pearson correlation between a linear projection of the generated trajectories and articulatory data derived from a multi-speaker dataset of electromagnetic articulography (EMA) recordings. A correlation of 0.67 is obtained with an extended feature set based on generative phonology and a linear interpolation technique. We discuss the implications of our results for our understanding of the dynamics of biological motion.
@inproceedings{ortiztandazo24_interspeech,title={Simulating articulatory trajectories with phonological feature interpolation},author={{Ortiz Tandazo}, Angelo and Schatz, Thomas and Hueber, Thomas and Dupoux, Emmanuel},year={2024},booktitle={Interspeech 2024},pages={3595--3599},doi={10.21437/Interspeech.2024-2192},issn={2958-1796},}