# Probing self-supervised speech models for phonetic and phonemic information: a case study in aspiration

arXiv: https://arxiv.org/abs/2306.06232 · HTML: https://mlsanity.com/paper/2306.06232/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [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)
- [Probing Classifiers: Promises, Shortcomings, and Advances](https://mlsanity.com/paper/2102.12452/) (2021)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [Comparative layer-wise analysis of self-supervised speech models](https://mlsanity.com/paper/2211.03929/) (2022)
- [Generative Pre-Training for Speech with Autoregressive Predictive Coding](https://mlsanity.com/paper/1910.12607/) (2019)
- [Toward a realistic model of speech processing in the brain with self-supervised learning](https://mlsanity.com/paper/2206.01685/) (2022)
- [Self-supervised models of audio effectively explain human cortical responses to speech](https://mlsanity.com/paper/2205.14252/) (2022)
- [Do self-supervised speech models develop human-like perception biases?](https://mlsanity.com/paper/2205.15819/) (2022)
- [Similarity Analysis of Self-Supervised Speech Representations](https://mlsanity.com/paper/2010.11481/) (2020)
- [Self-Supervised Representations Improve End-to-End Speech Translation](https://mlsanity.com/paper/2006.12124/) (2020)
- [Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification](https://mlsanity.com/paper/2008.03615/) (2020)

## Built on by (most cited first, 30 of 36)

- [Self-Supervised Speech Representations are More Phonetic than Semantic](https://mlsanity.com/paper/2406.08619/) (2024)
- [How Linguistics Learned to Stop Worrying and Love the Language Models](https://mlsanity.com/paper/2501.17047/) (2025)
- [Encoding of lexical tone in self-supervised models of spoken language](https://mlsanity.com/paper/2403.16865/) (2024)
- [Human-like Linguistic Biases in Neural Speech Models: Phonetic Categorization and Phonotactic Constraints in Wav2Vec2.0](https://mlsanity.com/paper/2407.03005/) (2024)
- [A layer-wise analysis of Mandarin and English suprasegmentals in SSL speech models](https://mlsanity.com/paper/2408.13678/) (2024)
- [Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment](https://mlsanity.com/paper/2502.07029/) (2025)
- [Emergent morpho-phonological representations in self-supervised speech models](https://mlsanity.com/paper/2509.22973/) (2025)
- [Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces](https://mlsanity.com/paper/2603.12642/) (2026)
- [Self-supervised Speech Representations Still Struggle with African American Vernacular English](https://mlsanity.com/paper/2408.14262/) (2024)
- [Layer-wise Minimal Pair Probing Reveals Contextual Grammatical-Conceptual Hierarchy in Speech Representations](https://mlsanity.com/paper/2509.15655/) (2025)
- [PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement](https://mlsanity.com/paper/2511.13300/) (2025)
- [Iterative refinement, not training objective, makes HuBERT behave differently from wav2vec 2.0](https://mlsanity.com/paper/2508.08110/) (2025)
- [Probing self-attention in self-supervised speech models for cross-linguistic differences](https://mlsanity.com/paper/2409.03115/) (2024)
- [Do Discrete Self-Supervised Representations of Speech Capture Tone Distinctions?](https://mlsanity.com/paper/2410.19935/) (2024)
- [Analyzing the relationships between pretraining language, phonetic, tonal, and speaker information in self-supervised speech models](https://mlsanity.com/paper/2506.10855/) (2025)
- [A predictive learning model can simulate temporal dynamics and context effects found in neural representations of continuous speech](https://mlsanity.com/paper/2405.08237/) (2024)
- [Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution](https://mlsanity.com/paper/2506.02181/) (2025)
- [A framework for analyzing concept representations in neural models](https://mlsanity.com/paper/2605.01381/) (2026)
- [From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models](https://mlsanity.com/paper/2506.01133/) (2025)
- [Identifying Speaker Information in Feed-Forward Layers of Self-Supervised Speech Transformers](https://mlsanity.com/paper/2506.21712/) (2025)
- [Tracking the emergence of linguistic structure in self-supervised models learning from speech](https://mlsanity.com/paper/2604.02043/) (2026)
- [Language models as tools for investigating the distinction between possible and impossible natural languages](https://mlsanity.com/paper/2512.09394/) (2025)
- [Large language models have learned to use language](https://mlsanity.com/paper/2512.12447/) (2025)
- [Large language models are not about natural language](https://mlsanity.com/paper/2512.13441/) (2025)
- [Linguists should learn to love speech-based deep learning models](https://mlsanity.com/paper/2512.14506/) (2025)
- [Categorize Early, Integrate Late: Divergent Processing Strategies in Automatic Speech Recognition](https://mlsanity.com/paper/2601.06972/) (2026)
- [Are Language Models Models?](https://mlsanity.com/paper/2601.10421/) (2026)
- [Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities](https://mlsanity.com/paper/2604.09466/) (2026)
- [You Can't Fight in Here! This is BBS!](https://mlsanity.com/paper/2604.09501/) (2026)
- [Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear](https://mlsanity.com/paper/2605.10061/) (2026)

## Abstract (arXiv)

Textless self-supervised speech models have grown in capabilities in recent years, but the nature of the linguistic information they encode has not yet been thoroughly examined. We evaluate the extent to which these models' learned representations align with basic representational distinctions made by humans, focusing on a set of phonetic (low-level) and phonemic (more abstract) contrasts instantiated in word-initial stops. We find that robust representations of both phonetic and phonemic distinctions emerge in early layers of these models' architectures, and are preserved in the principal components of deeper layer representations. Our analyses suggest two sources for this success: some can only be explained by the optimization of the models on speech data, while some can be attributed to these models' high-dimensional architectures. Our findings show that speech-trained HuBERT derives a low-noise and low-dimensional subspace corresponding to abstract phonological distinctions.