mlsanityData to 14 Oct 2026

Word stress in self-supervised speech models: A cross-linguistic comparison

arXiv 2507.04738 ↗First posted 7 Jul 2025cs.CL cs.AIcited by 3

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Abstract (arXiv)

In this paper we study word stress representations learned by self-supervised speech models (S3M), specifically the Wav2vec 2.0 model. We investigate the S3M representations of word stress for five different languages: Three languages with variable or lexical stress (Dutch, English and German) and two languages with fixed or demarcative stress (Hungarian and Polish). We train diagnostic stress classifiers on S3M embeddings and show that they can distinguish between stressed and unstressed syllables in read-aloud short sentences with high accuracy. We also tested language-specificity effects of S3M word stress. The results indicate that the word stress representations are language-specific, with a greater difference between the set of variable versus the set of fixed stressed languages.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 10 of its 38 references that are in the map. First posted on arXiv 7 Jul 2025.

Builds on

10 of 38 references.

References · 10

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  2. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
  3. 2016Understanding intermediate layers using linear classifier probescited by 1,353
  4. 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 601
  5. 2018What you can cram into a single vector: Probing sentence embeddings for linguistic propertiescited by 457
  6. 2021Layer-wise Analysis of a Self-supervised Speech Representation Modelcited by 298
  7. 2021Large-scale Self-Supervised Speech Representation Learning for Automatic Speaker Verificationcited by 78
  8. 2024Self-Supervised Speech Representations are More Phonetic than Semanticcited by 71
  9. 2023Wave to Syntax: Probing spoken language models for syntaxcited by 18
  10. 2023An Information-Theoretic Analysis of Self-supervised Discrete Representations of Speechcited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representationscited by 3
  2. 2026Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probecited by 0
  3. 2026Perceptual compensation for tonal context in self-supervised speech modelscited by 0