Tracking the emergence of linguistic structure in self-supervised models learning from speech
Hey there, little explorer! Imagine you have a super-smart robot friend who wants to learn how to talk, just like you!
This science story is about how these robot friends, called "AI models," learn from listening to people speak. It's like they're listening to lots and lots of Dutch stories and songs.
The scientists wanted to know: When does the robot start understanding different things? Like, when does it learn the sounds? When does it learn whole words? And when does it learn to make sentences?
They found out that the robot learns different things at different times, like building blocks! First, it learns the tiny sounds, then it puts them together to make words, and then it learns how words fit to make sense. It's like watching a baby learn to talk, but super fast! Isn't that cool?
arXiv:2604.02043v1 Announce Type: cross Abstract: Self-supervised speech models learn effective representations of spoken language, which have been shown to reflect various aspects of linguistic structure. But when does such structure emerge in model training? We study the encoding of a wide range of linguistic structures, across layers and intermediate checkpoints of six Wav2Vec2 and HuBERT models trained on spoken Dutch. We find that different levels of linguistic structure show notably distinct layerwise patterns as well as learning trajectories, which can partially be explained by differences in their degree of abstraction from the acoustic signal and the timescale at which information from the input is integrated. Moreover, we find that the level at which pre-training objectives are d
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Abstract:Self-supervised speech models learn effective representations of spoken language, which have been shown to reflect various aspects of linguistic structure. But when does such structure emerge in model training? We study the encoding of a wide range of linguistic structures, across layers and intermediate checkpoints of six Wav2Vec2 and HuBERT models trained on spoken Dutch. We find that different levels of linguistic structure show notably distinct layerwise patterns as well as learning trajectories, which can partially be explained by differences in their degree of abstraction from the acoustic signal and the timescale at which information from the input is integrated. Moreover, we find that the level at which pre-training objectives are defined strongly affects both the layerwise organization and the learning trajectories of linguistic structures, with greater parallelism induced by higher-order prediction tasks (i.e. iteratively refined pseudo-labels).
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2604.02043 [cs.CL]
(or arXiv:2604.02043v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2604.02043
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Marianne De Heer Kloots [view email] [v1] Thu, 2 Apr 2026 13:48:22 UTC (7,082 KB)
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