mlsanityData to 14 Oct 2026

fastabx: A library for efficient computation of ABX discriminability

arXiv 2505.02692 ↗First posted 5 May 2025cs.CL cs.SD eess.AScited by 7

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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.

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

18 of 34 references.

References · 18

  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. 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 666
  5. 2024Moshi: a speech-text foundation model for real-time dialoguecited by 568
  6. 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 467
  7. 2021Generative Spoken Language Modeling from Raw Audiocited by 340
  8. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 260
  9. 2020The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modelingcited by 101
  10. 2023What Do Self-Supervised Speech Models Know About Words?cited by 64
  11. 2017The Zero Resource Speech Challenge 2017cited by 45
  12. 2019The Zero Resource Speech Challenge 2019: TTS without Tcited by 38
6 more
  1. 2022Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challengecited by 34
  2. 2022Do self-supervised speech models develop human-like perception biases?cited by 28
  3. 2023Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clusteringcited by 22
  4. 2023Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Headscited by 17
  5. 2022Evaluating context-invariance in unsupervised speech representationscited by 11
  6. 2024Improving Spoken Language Modeling with Phoneme Classification: A Simple Fine-tuning Approachcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025SpidR: Learning Fast and Stable Linguistic Units for Spoken Language Models Without Supervisioncited by 8
  2. 2026Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representationscited by 3
  3. 2025Leveraging Audio-Visual Data to Reduce the Multilingual Gap in Self-Supervised Speech Modelscited by 1
  4. 2025MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discoverycited by 1
  5. 2025SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptationcited by 1
  6. 2026DiscoPhon: Benchmarking the Unsupervised Discovery of Phoneme Inventories With Discrete Speech Unitscited by 0
  7. 2026EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Datacited by 0