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

arXiv: https://arxiv.org/abs/2604.02102 · HTML: https://mlsanity.com/paper/2604.02102/

## 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 (most cited first, 22 of 22)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Comparative layer-wise analysis of self-supervised speech models](https://mlsanity.com/paper/2211.03929/) (2022)
- [Self-Supervised Speech Representations are More Phonetic than Semantic](https://mlsanity.com/paper/2406.08619/) (2024)
- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [On the Utility of Self-supervised Models for Prosody-related Tasks](https://mlsanity.com/paper/2210.07185/) (2022)
- [Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge](https://mlsanity.com/paper/2210.15759/) (2022)
- [Encoding of lexical tone in self-supervised models of spoken language](https://mlsanity.com/paper/2403.16865/) (2024)
- [Release of Pre-Trained Models for the Japanese Language](https://mlsanity.com/paper/2404.01657/) (2024)
- [A layer-wise analysis of Mandarin and English suprasegmentals in SSL speech models](https://mlsanity.com/paper/2408.13678/) (2024)
- [Neural Representations for Modeling Variation in Speech](https://mlsanity.com/paper/2011.12649/) (2020)
- [Understanding Probe Behaviors through Variational Bounds of Mutual Information](https://mlsanity.com/paper/2312.10019/) (2023)
- [fastabx: A library for efficient computation of ABX discriminability](https://mlsanity.com/paper/2505.02692/) (2025)
- [Perceptimatic: A human speech perception benchmark for unsupervised subword modelling](https://mlsanity.com/paper/2010.05961/) (2020)
- [k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2411.17100/) (2024)
- [Word stress in self-supervised speech models: A cross-linguistic comparison](https://mlsanity.com/paper/2507.04738/) (2025)
- [Benchmarking Prosody Encoding in Discrete Speech Tokens](https://mlsanity.com/paper/2508.11224/) (2025)

## Built on by (most cited first, 3 of 3)

- [Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring](https://mlsanity.com/paper/2607.13721/) (2026)
- [Phone Segmentation and Recognition through Phonological Activation Mapping](https://mlsanity.com/paper/2607.09020/) (2026)
- [Tone on a Budget: A Reference-Free Metric for Lexical Tone in Massively Multilingual Text-to-Speech](https://mlsanity.com/paper/2609.14817/) (2026)

## 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.