# fastabx: A library for efficient computation of ABX discriminability

arXiv: https://arxiv.org/abs/2505.02692 · HTML: https://mlsanity.com/paper/2505.02692/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 18 of 18)

- [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)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Moshi: a speech-text foundation model for real-time dialogue](https://mlsanity.com/paper/2410.00037/) (2024)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling](https://mlsanity.com/paper/2011.11588/) (2020)
- [What Do Self-Supervised Speech Models Know About Words?](https://mlsanity.com/paper/2307.00162/) (2023)
- [The Zero Resource Speech Challenge 2017](https://mlsanity.com/paper/1712.04313/) (2017)
- [The Zero Resource Speech Challenge 2019: TTS without T](https://mlsanity.com/paper/1904.11469/) (2019)
- [Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge](https://mlsanity.com/paper/2210.15759/) (2022)
- [Do self-supervised speech models develop human-like perception biases?](https://mlsanity.com/paper/2205.15819/) (2022)
- [Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering](https://mlsanity.com/paper/2305.11072/) (2023)
- [Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Heads](https://mlsanity.com/paper/2308.14456/) (2023)
- [Evaluating context-invariance in unsupervised speech representations](https://mlsanity.com/paper/2210.15775/) (2022)
- [Improving Spoken Language Modeling with Phoneme Classification: A Simple Fine-tuning Approach](https://mlsanity.com/paper/2410.00025/) (2024)

## Built on by (most cited first, 7 of 7)

- [SpidR: Learning Fast and Stable Linguistic Units for Spoken Language Models Without Supervision](https://mlsanity.com/paper/2512.20308/) (2025)
- [Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representations](https://mlsanity.com/paper/2604.02102/) (2026)
- [Leveraging Audio-Visual Data to Reduce the Multilingual Gap in Self-Supervised Speech Models](https://mlsanity.com/paper/2509.17523/) (2025)
- [MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery](https://mlsanity.com/paper/2512.19612/) (2025)
- [SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation](https://mlsanity.com/paper/2512.21204/) (2025)
- [DiscoPhon: Benchmarking the Unsupervised Discovery of Phoneme Inventories With Discrete Speech Units](https://mlsanity.com/paper/2603.18612/) (2026)
- [EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data](https://mlsanity.com/paper/2605.19130/) (2026)

## Abstract (arXiv)

We introduce fastabx, a high-performance Python library for building ABX discrimination tasks. ABX is a measure of the separation between generic categories of interest. It has been used extensively to evaluate phonetic discriminability in self-supervised speech representations. However, its broader adoption has been limited by the absence of adequate tools. fastabx addresses this gap by providing a framework capable of constructing any type of ABX task while delivering the efficiency necessary for rapid development cycles, both in task creation and in calculating distances between representations. We believe that fastabx will serve as a valuable resource for the broader representation learning community, enabling researchers to systematically investigate what information can be directly extracted from learned representations across several domains beyond speech processing. The source code is available at https://github.com/bootphon/fastabx.