mlsanityData to 14 Oct 2026

BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models

arXiv 2306.01506 ↗First posted 2 Jun 2023cs.CL eess.AS stat.MLcited by 29

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Abstract (arXiv)

Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels. In order to fully realize the potential of these approaches and further our understanding of how infants learn language, simulations must closely emulate real-life situations by training on developmentally plausible corpora and benchmarking against appropriate test sets. To this end, we propose a language-acquisition-friendly benchmark to probe spoken language models at the lexical and syntactic levels, both of which are compatible with the vocabulary typical of children's language experiences. This paper introduces the benchmark and summarizes a range of experiments showing its usefulness. In addition, we highlight two exciting challenges that need to be addressed for further progress: bridging the gap between text and speech and between clean speech and in-the-wild speech.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

12 of 30 references.

References · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 467
  3. 2021Generative Spoken Language Modeling from Raw Audiocited by 340
  4. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 260
  5. 2021Text-Free Prosody-Aware Generative Spoken Language Modelingcited by 119
  6. 2020The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modelingcited by 101
  7. 2022What Artificial Neural Networks Can Tell Us About Human Language Acquisitioncited by 99
  8. 2016Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learnercited by 68
  9. 2023Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpuscited by 62
  10. 2021The Zero Resource Speech Challenge 2021: Spoken language modellingcited by 47
  11. 2020An open-source voice type classifier for child-centered daylong recordingscited by 14
  12. 2022Learning English with Peppa Pigcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 11; 2026: 12.

29 papers.

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  1. 2025How Linguistics Learned to Stop Worrying and Love the Language Modelscited by 51
  2. 2024Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corporacited by 48
  3. 2025Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Surveycited by 39
  4. 2024A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debatescited by 23
  5. 2024From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemescited by 11
  6. 2025Do Construction Distributions Shape Formal Language Learning In German BabyLMs?cited by 11
  7. 2026Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive sciencecited by 11
  8. 2025Subword models struggle with word learning, but surprisal hides itcited by 6
  9. 2025IPA-CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modelingcited by 6
  10. 2024Small Language Models Also Work With Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamascited by 5
  11. 2024Children's Speech Recognition through Discrete Token Enhancementcited by 4
  12. 2026PRiSM: Benchmarking Phone Realization in Speech Modelscited by 4
17 more
  1. 2024A model of early word acquisition based on realistic-scale audiovisual naming eventscited by 3
  2. 2026HuPER: A Human-Inspired Framework for Phonetic Perceptioncited by 3
  3. 2025BabyLM's First Words: Word Segmentation as a Phonological Probing Taskcited by 2
  4. 2026Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priorscited by 1
  5. 2025Do Self-Supervised Speech Models Exhibit the Critical Period Effects in Language Acquisition?cited by 0
  6. 2025Language models as tools for investigating the distinction between possible and impossible natural languagescited by 0
  7. 2025Large language models have learned to use languagecited by 0
  8. 2025Large language models are not about natural languagecited by 0
  9. 2025Linguists should learn to love speech-based deep learning modelscited by 0
  10. 2026Are Language Models Models?cited by 0
  11. 2026Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilitiescited by 0
  12. 2026You Can't Fight in Here! This is BBS!cited by 0
  13. 2026Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appearcited by 0
  14. 2026EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Datacited by 0
  15. 2026On the Effect of Segmentation Width and Cluster Size on Speech Resynthesis and Continuation in Generative Spoken Language Modelscited by 0
  16. 2026A retrospective analysis on the use of LLMs to study infant syntax learningcited by 0
  17. 2026Cross-Linguistic Effects in Bilingual Phoneme BabyLMscited by 0