mlsanityData to 14 Oct 2026

Layer-wise Minimal Pair Probing Reveals Contextual Grammatical-Conceptual Hierarchy in Speech Representations

arXiv 2509.15655 ↗First posted 19 Sep 2025cs.CL eess.AScited by 5

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Abstract (arXiv)

Transformer-based speech language models (SLMs) have significantly improved neural speech recognition and understanding. While existing research has examined how well SLMs encode shallow acoustic and phonetic features, the extent to which SLMs encode nuanced syntactic and conceptual features remains unclear. By drawing parallels with linguistic competence assessments for large language models, this study is the first to systematically evaluate the presence of contextual syntactic and semantic features across SLMs for self-supervised learning (S3M), automatic speech recognition (ASR), speech compression (codec), and as the encoder for auditory large language models (AudioLLMs). Through minimal pair designs and diagnostic feature analysis across 71 tasks spanning diverse linguistic levels, our layer-wise and time-resolved analysis uncovers that 1) all speech encode grammatical features more robustly than conceptual ones.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 44 of its 55 references that are in the map. First posted on arXiv 19 Sep 2025.

Builds on

44 of 55 references.

References · 44

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  3. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  4. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  5. 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 876
  6. 2023Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Modelscited by 608
  7. 2019BLiMP: The Benchmark of Linguistic Minimal Pairs for Englishcited by 564
  8. 2016Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependenciescited by 354
  9. 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 326
  10. 2019What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language modelscited by 320
  11. 2021Layer-wise Analysis of a Self-supervised Speech Representation Modelcited by 298
  12. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 260
32 more
  1. 2018Targeted Syntactic Evaluation of Language Modelscited by 242
  2. 2018Colorless green recurrent networks dream hierarchicallycited by 213
  3. 2020fairseq S2T: Fast Speech-to-Text Modeling with fairseqcited by 176
  4. 2020A Systematic Assessment of Syntactic Generalization in Neural Language Modelscited by 165
  5. 2022Comparative layer-wise analysis of self-supervised speech modelscited by 158
  6. 2023StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Modelscited by 152
  7. 2018What do RNN Language Models Learn about Filler-Gap Dependencies?cited by 86
  8. 2024Self-Supervised Speech Representations are More Phonetic than Semanticcited by 71
  9. 2023What Do Self-Supervised Speech Models Know About Words?cited by 64
  10. 2021What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structurecited by 52
  11. 2021CLiMP: A Benchmark for Chinese Language Model Evaluationcited by 46
  12. 2023Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Studycited by 42
  13. 2024Towards audio language modeling -- an overviewcited by 40
  14. 2023Probing self-supervised speech models for phonetic and phonemic information: a case study in aspirationcited by 36
  15. 2022Probing phoneme, language and speaker information in unsupervised speech representationscited by 33
  16. 2017Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systemscited by 32
  17. 2021Speech-language Pre-training for End-to-end Spoken Language Understandingcited by 27
  18. 2020Similarity Analysis of Self-Supervised Speech Representationscited by 26
  19. 2020Probing Acoustic Representations for Phonetic Propertiescited by 26
  20. 2024Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairscited by 24
  21. 2019Probing the Information Encoded in X-vectorscited by 18
  22. 2022Opening the Black Box of wav2vec Feature Encodercited by 18
  23. 2023Wave to Syntax: Probing spoken language models for syntaxcited by 18
  24. 2023Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Headscited by 17
  25. 2019Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognitioncited by 16
  26. 2024Just ASR + LLM? A Study on Speech Large Language Models' Ability to Identify and Understand Speaker in Spoken Dialoguecited by 16
  27. 2020Understanding Self-Attention of Self-Supervised Audio Transformerscited by 15
  28. 2023An Information-Theoretic Analysis of Self-supervised Discrete Representations of Speechcited by 15
  29. 2023Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformerscited by 13
  30. 2024A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinesecited by 13
  31. 2023Understanding Probe Behaviors through Variational Bounds of Mutual Informationcited by 11
  32. 2023SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?cited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Abstraction Induces the Brain Alignment of Language and Speech Modelscited by 2
  2. 2026Resurfacing Paralinguistic Awareness in Large Audio Language Modelscited by 2
  3. 2026Tracking the emergence of linguistic structure in self-supervised models learning from speechcited by 1
  4. 2026Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classificationcited by 0
  5. 2026Grammatical "grandmother neurons" are rare in LLMscited by 0