aiqbench analysis
The paper finds that deep speech models, including HuBERT and nine others, encode word timing as a roughly circular trajectory in a low-dimensional subspace. A simple method built on this structure, Rotary Segmentation (RoSeg), reaches a 64.3% boundary F-score on Librispeech. The authors note that a supervised step is still needed to find a good subspace, so the result is evidence of implicit word knowledge rather than fully unsupervised segmentation. For readers, it is a useful interpretability result about what speech representations contain, not a new production system.
Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory
arXiv:2610.16273v1 Announce Type: new Abstract: Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains underexplored. We study this question using the framework of representational geometry, which investigates whether and how concepts are represented in low-dimensional subspaces of the representation space. Prior work in cognitive neuroscience has shown that when HuBERT feature sequences are averaged across words of the same duration, they form near-circular trajectories from word onset to word offset in a two-dimensional "temporal subspace." Here we show that this circular structure of word timing is a general property of deep speech representations, by demonstrating its emergence in nine additional speech models covering both supervised and self-supervised. We then move beyond averaged word segments and show that in HuBERT, the circular trajectories are consistent enough at the level of individual words to perform word segmentation. Our approach, Rotary Segmentation (RoSeg), achieves 64.3% boundary F-score on Librispeech by detecting word boundaries when cycles complete in the temporal subspace. Although supervision is needed to identify a good subspace, nevertheless this result demonstrates much stronger implicit word knowledge in HuBERT than has previously been shown.
Abstract:Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains underexplored. We study this question using the framework of representational geometry, which investigates whether and how concepts are represented in low-dimensional subspaces of the representation space. Prior work in cognitive neuroscience has shown that when HuBERT feature sequences are averaged across words of the same duration, they form near-circular trajectories from word onset to word offset in a two-dimensional "temporal subspace." Here we show that this circular structure of word timing is a general property of deep speech representations, by demonstrating its emergence in nine additional speech models covering both supervised and self-supervised. We then move beyond averaged word segments and show that in HuBERT, the circular trajectories are consistent enough at the level of individual words to perform word segmentation. Our approach, Rotary Segmentation (RoSeg), achieves 64.3% boundary F-score on Librispeech by detecting word boundaries when cycles complete in the temporal subspace. Although supervision is needed to identify a good subspace, nevertheless this result demonstrates much stronger implicit word knowledge in HuBERT than has previously been shown.
Computation and Language (cs.CL); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
arXiv:2610.16273 [cs.CL]
(or arXiv:2610.16273v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.16273 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 292 words
