mlsanityData to 14 Oct 2026

Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representations

arXiv 2604.02102 ↗First posted 2 Apr 2026cs.CL cs.LG cs.SDcited by 3

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Abstract (arXiv)

Speech representations from self-supervised speech models (S3Ms) are known to be sensitive to phonemic contrasts, but their sensitivity to prosodic contrasts has not been directly measured. The ABX discrimination task has been used to measure phonemic contrast in S3M representations via minimal pairs. We introduce prosodic ABX, an extension of this framework to evaluate prosodic contrast with only a handful of examples and no explicit labels. Also, we build and release a dataset of English and Japanese minimal pairs and use it along with a Mandarin dataset to evaluate contrast in English stress, Japanese pitch accent, and Mandarin tone. Finally, we show that model and layer rankings are often preserved across several experimental conditions, making it practical for low-resource settings.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 41 references that are in the map. First posted on arXiv 2 Apr 2026.

Builds on

22 of 41 references.

References · 22

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  2. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  3. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  4. 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 601
  5. 2021Generative Spoken Language Modeling from Raw Audiocited by 340
  6. 2021Layer-wise Analysis of a Self-supervised Speech Representation Modelcited by 298
  7. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 260
  8. 2022Comparative layer-wise analysis of self-supervised speech modelscited by 158
  9. 2024Self-Supervised Speech Representations are More Phonetic than Semanticcited by 71
  10. 2024mHuBERT-147: A Compact Multilingual HuBERT Modelcited by 66
  11. 2022On the Utility of Self-supervised Models for Prosody-related Taskscited by 42
  12. 2022Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challengecited by 34
10 more
  1. 2024Encoding of lexical tone in self-supervised models of spoken languagecited by 23
  2. 2024Release of Pre-Trained Models for the Japanese Languagecited by 22
  3. 2024A layer-wise analysis of Mandarin and English suprasegmentals in SSL speech modelscited by 18
  4. 2020Neural Representations for Modeling Variation in Speechcited by 14
  5. 2023Understanding Probe Behaviors through Variational Bounds of Mutual Informationcited by 11
  6. 2025fastabx: A library for efficient computation of ABX discriminabilitycited by 7
  7. 2020Perceptimatic: A human speech perception benchmark for unsupervised subword modellingcited by 6
  8. 2024k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learningcited by 6
  9. 2025Word stress in self-supervised speech models: A cross-linguistic comparisoncited by 3
  10. 2025Benchmarking Prosody Encoding in Discrete Speech Tokenscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoringcited by 2
  2. 2026Phone Segmentation and Recognition through Phonological Activation Mappingcited by 0
  3. 2026Tone on a Budget: A Reference-Free Metric for Lexical Tone in Massively Multilingual Text-to-Speechcited by 0