# A Native-Reference Phone-Class Geometry for Second-Language Pronunciation Analysis

arXiv: https://arxiv.org/abs/2609.30075 · HTML: https://mlsanity.com/paper/2609.30075/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 24 Sep 2026.

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

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [Comparative layer-wise analysis of self-supervised speech models](https://mlsanity.com/paper/2211.03929/) (2022)
- [What Do Self-Supervised Speech Models Know About Words?](https://mlsanity.com/paper/2307.00162/) (2023)
- [Lhotse: a speech data representation library for the modern deep learning ecosystem](https://mlsanity.com/paper/2110.12561/) (2021)
- [FireRedASR2S: A State-of-the-Art Industrial-Grade All-in-One Automatic Speech Recognition System](https://mlsanity.com/paper/2603.10420/) (2026)
- [Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment](https://mlsanity.com/paper/2502.07029/) (2025)
- [[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic](https://mlsanity.com/paper/2602.18899/) (2026)
- [Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use](https://mlsanity.com/paper/2505.21578/) (2025)
- [Investigating the Effect of Label Topology and Training Criterion on ASR Performance and Alignment Quality](https://mlsanity.com/paper/2407.11641/) (2024)
- [Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring](https://mlsanity.com/paper/2607.13721/) (2026)
- [A Native-Reference Coordinate Geometry for L2 Pronunciation Deviation Using Self-Supervised Speech Models](https://mlsanity.com/paper/2609.28060/) (2026)

## Abstract (arXiv)

Automatic speaking assessment systems can provide holistic proficiency scores, but often lack interpretable measures that characterize pronunciation quality. We propose a native-reference phone-class geometry for measuring second language (L2) pronunciation deviation without requiring pronunciation labels, read-aloud prompts, or matched recordings of the same text from native and L2 speakers. Given a native speech corpus, we average frame-level self-supervised representations for each context-dependent phone-class and use singular value decomposition (SVD) to derive a compact native-reference coordinate system. For each L2 utterance, we compute the corresponding averages and project them into the native-reference space. We then demonstrate that the distances between L2 and native-reference coordinates for matched phone-classes show consistent negative correlations with holistic speaking proficiency on the Dev subset of the Speak and Improve Corpus 2025 (Spearman's ρ!=!-0.53) and with pronunciation quality on the learner subset of the English Read by Japanese Students dataset (ρ!=!-0.34). These findings suggest that the proposed geometry captures acoustic-phonetic information relevant for proficiency rating while remaining applicable to spontaneous L2 speech without matched native recordings.