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• Different levels of linguistic structure show distinct layerwise patterns in self-supervised speech modelsresult 90%
• The emergence of linguistic structure in self-supervised models can be partially explained by differences in abstraction from the acoustic signalresult 85%
• The timescale at which information from input is integrated affects the learning trajectories of linguistic structuresresult 85%
• Pre-training objective definition level strongly affects layerwise organization of linguistic structuresresult 90%
• Higher-order prediction tasks induce greater parallelism in linguistic structure learningresult 85%
• The study examines six Wav2Vec2 and HuBERT models trained on spoken Dutchmethod 95%
• Self-supervised speech models learn effective representations of spoken language that reflect various aspects of linguistic structuretechnical 90%
• The research investigates when linguistic structure emerges during model trainingmethod 90%

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