# On the Relationship between Accent Strength and Articulatory Features

arXiv: https://arxiv.org/abs/2507.03149 · HTML: https://mlsanity.com/paper/2507.03149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [Coding Speech through Vocal Tract Kinematics](https://mlsanity.com/paper/2406.12998/) (2024)
- [Speaker-Independent Acoustic-to-Articulatory Speech Inversion](https://mlsanity.com/paper/2302.06774/) (2023)
- [Towards a Quantitative Analysis of Coarticulation with a Phoneme-to-Articulatory Model](https://mlsanity.com/paper/2408.05641/) (2024)
- [Articulatory Configurations across Genders and Periods in French Radio and TV archives](https://mlsanity.com/paper/2408.04519/) (2024)

## Built on by (most cited first, 2 of 2)

- [An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production](https://mlsanity.com/paper/2603.04840/) (2026)
- [ARTI-6: Towards Six-dimensional Articulatory Speech Encoding](https://mlsanity.com/paper/2509.21447/) (2025)

## Abstract (arXiv)

This paper explores the relationship between accent strength and articulatory features inferred from acoustic speech. To quantify accent strength, we compare phonetic transcriptions with transcriptions based on dictionary-based references, computing phoneme-level difference as a measure of accent strength. The proposed framework leverages recent self-supervised learning articulatory inversion techniques to estimate articulatory features. Analyzing a corpus of read speech from American and British English speakers, this study examines correlations between derived articulatory parameters and accent strength proxies, associating systematic articulatory differences with indexed accent strength. Results indicate that tongue positioning patterns distinguish the two dialects, with notable differences inter-dialects in rhotic and low back vowels. These findings contribute to automated accent analysis and articulatory modeling for speech processing applications.