Papers by Maxime Poli
SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (2026.acl-long)
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Mahi Luthra, Jiayi Shen, Maxime Poli, Angelo Ortiz Tandazo, Yosuke Higuchi, Youssef Benchekroun, Martin Gleize, Charles-Éric Saint-James, Dongyan Lin, Phillip Rust, Angel Villar-Corrales, null Surya, Vanessa Stark, Rashel Moritz, Juan Pino, Yann LeCun, Emmanuel Dupoux
| Challenge: | Empirically, SpidR-Adapt achieves rapid gains in phonemic discriminability and downstream spoken language modeling scores . current self-supervised learning models require thousands of hours of training data to learn meaningful linguistic representations. |
| Approach: | They propose a bi-level optimization framework for rapid adaptation of speech units to new languages using minimal unlabeled data. |
| Outcome: | The proposed model achieves rapid gains in phonemic discriminability and spoken language modeling scores . it surpasses in-domain toplines after training on less than 1h of target-language audio . |
Improving Spoken Language Modeling with Phoneme Classification: A Simple Fine-tuning Approach (2024.emnlp-main)
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| Challenge: | Generating speech through a pipeline that operates at the text level typically loses nuances, intonations, and non-verbal vocalizations. |
| Approach: | They show that fine-tuning speech representation models on phoneme classification leads to more context-invariant representations, and language models trained on these units achieve comparable lexical comprehension to ones trained on hundred times more data. |
| Outcome: | Recent advances in speech representation modeling have shown that learning language directly from speech is feasible. |