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Arxiv
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The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
- The 2025 PNPL competition aims at advancing speech decoding from non-invasive brain data to help paralyzed individuals with speech deficits.
- The competition seeks to avoid high-risk surgical interventions and restore communication through machine learning.
- It aims to achieve an 'ImageNet moment' in non-invasive neural decoding by uniting the machine learning community.
- The LibriBrain dataset, coupled with the pnpl Python library, is provided to assist participants with training and deep learning model integration.
- Two fundamental tasks - Speech Detection and Phoneme Classification from brain data - are defined for the competition.
- The competition includes standard data splits, evaluation metrics, benchmark models, tutorial code, a discussion board, and a public leaderboard for submissions.
- There are two competition tracks: a Standard track focusing on algorithmic innovation, and an Extended track encouraging larger-scale computing for progressing toward a non-invasive brain-computer interface for speech.
- The competition is designed to promote accessibility and foster participation within the machine learning community.
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