# Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment

arXiv: https://arxiv.org/abs/2502.07029 · HTML: https://mlsanity.com/paper/2502.07029/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 72 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [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)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Out-of-Distribution Detection with Deep Nearest Neighbors](https://mlsanity.com/paper/2204.06507/) (2022)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Fast Differentiable Sorting and Ranking](https://mlsanity.com/paper/2002.08871/) (2020)
- [Comparative layer-wise analysis of self-supervised speech models](https://mlsanity.com/paper/2211.03929/) (2022)
- [Self-Supervised Speech Representations are More Phonetic than Semantic](https://mlsanity.com/paper/2406.08619/) (2024)
- [What Do Self-Supervised Speech Models Know About Words?](https://mlsanity.com/paper/2307.00162/) (2023)
- [speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment](https://mlsanity.com/paper/2104.01378/) (2021)
- [Analysing Discrete Self Supervised Speech Representation for Spoken Language Modeling](https://mlsanity.com/paper/2301.00591/) (2023)
- [Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study](https://mlsanity.com/paper/2309.15800/) (2023)
- [Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment](https://mlsanity.com/paper/2205.03432/) (2022)
- [Probing self-supervised speech models for phonetic and phonemic information: a case study in aspiration](https://mlsanity.com/paper/2306.06232/) (2023)
- [Context-aware Goodness of Pronunciation for Computer-Assisted Pronunciation Training](https://mlsanity.com/paper/2008.08647/) (2020)
- [SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2210.08634/) (2022)
- [3M: An Effective Multi-view, Multi-granularity, and Multi-aspect Modeling Approach to English Pronunciation Assessment](https://mlsanity.com/paper/2208.09110/) (2022)
- [Self-supervised Predictive Coding Models Encode Speaker and Phonetic Information in Orthogonal Subspaces](https://mlsanity.com/paper/2305.12464/) (2023)
- [Opening the Black Box of wav2vec Feature Encoder](https://mlsanity.com/paper/2210.15386/) (2022)
- [An Information-Theoretic Analysis of Self-supervised Discrete Representations of Speech](https://mlsanity.com/paper/2306.02405/) (2023)
- [Understanding Probe Behaviors through Variational Bounds of Mutual Information](https://mlsanity.com/paper/2312.10019/) (2023)
- [Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification](https://mlsanity.com/paper/2305.18392/) (2023)
- [Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling](https://mlsanity.com/paper/2101.06396/) (2021)
- [Phonological Level wav2vec2-based Mispronunciation Detection and Diagnosis Method](https://mlsanity.com/paper/2311.07037/) (2023)
- [Comparison of L2 Korean pronunciation error patterns from five L1 backgrounds by using automatic phonetic transcription](https://mlsanity.com/paper/2306.10821/) (2023)

## Built on by (most cited first, 15 of 15)

- [POWSM: A Phonetic Open Whisper-Style Speech Foundation Model](https://mlsanity.com/paper/2510.24992/) (2025)
- [[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic](https://mlsanity.com/paper/2602.18899/) (2026)
- [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)
- [On-device Streaming Discrete Speech Units](https://mlsanity.com/paper/2506.01845/) (2025)
- [Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis](https://mlsanity.com/paper/2506.12073/) (2025)
- [PRiSM: Benchmarking Phone Realization in Speech Models](https://mlsanity.com/paper/2601.14046/) (2026)
- [Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease](https://mlsanity.com/paper/2603.22225/) (2026)
- [Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages](https://mlsanity.com/paper/2505.14874/) (2025)
- [Multilingual Phonological Feature Recognition with Self-Supervised Speech Models](https://mlsanity.com/paper/2605.25596/) (2026)
- [Tracking the emergence of linguistic structure in self-supervised models learning from speech](https://mlsanity.com/paper/2604.02043/) (2026)
- [A Native-Reference Coordinate Geometry for L2 Pronunciation Deviation Using Self-Supervised Speech Models](https://mlsanity.com/paper/2609.28060/) (2026)
- [Revisiting Rule-Based Stuttering Detection: A Comprehensive Analysis of Interpretable Models for Clinical Applications](https://mlsanity.com/paper/2508.16681/) (2025)
- [Phone Segmentation and Recognition through Phonological Activation Mapping](https://mlsanity.com/paper/2607.09020/) (2026)
- [A Native-Reference Phone-Class Geometry for Second-Language Pronunciation Analysis](https://mlsanity.com/paper/2609.30075/) (2026)

## Abstract (arXiv)

Allophony refers to the variation in the phonetic realization of a phoneme based on its phonetic environment. Modeling allophones is crucial for atypical pronunciation assessment, which involves distinguishing atypical from typical pronunciations. However, recent phoneme classifier-based approaches often simplify this by treating various realizations as a single phoneme, bypassing the complexity of modeling allophonic variation. Motivated by the acoustic modeling capabilities of frozen self-supervised speech model (S3M) features, we propose MixGoP, a novel approach that leverages Gaussian mixture models to model phoneme distributions with multiple subclusters. Our experiments show that MixGoP achieves state-of-the-art performance across four out of five datasets, including dysarthric and non-native speech. Our analysis further suggests that S3M features capture allophonic variation more effectively than MFCCs and Mel spectrograms, highlighting the benefits of integrating MixGoP with S3M features.