# Cross-lingual Dysarthria Severity Classification for English, Korean, and Tamil

arXiv: https://arxiv.org/abs/2209.12942 · HTML: https://mlsanity.com/paper/2209.12942/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 54 references that are in the map.
- First posted on arXiv 26 Sep 2022.

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

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Investigation of Data Augmentation Techniques for Disordered Speech Recognition](https://mlsanity.com/paper/2201.05562/) (2022)
- [Adversarial Data Augmentation for Disordered Speech Recognition](https://mlsanity.com/paper/2108.00899/) (2021)
- [Simulating dysarthric speech for training data augmentation in clinical speech applications](https://mlsanity.com/paper/1804.10325/) (2018)
- [An explainable XGBoost-based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellitus](https://mlsanity.com/paper/2009.06629/) (2020)

## Built on by (most cited first, 8 of 8)

- [Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task Learning](https://mlsanity.com/paper/2210.15387/) (2022)
- [Multilingual Dysarthric Speech Assessment Using Universal Phone Recognition and Language-Specific Phonemic Contrast Modeling](https://mlsanity.com/paper/2601.21205/) (2026)
- [Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease](https://mlsanity.com/paper/2603.22225/) (2026)
- [Speaker-Independent Dysarthria Severity Classification using Self-Supervised Transformers and Multi-Task Learning](https://mlsanity.com/paper/2403.00854/) (2024)
- [Training-Free Cross-Lingual Dysarthria Severity Assessment via Phonological Subspace Analysis in Self-Supervised Speech Representations](https://mlsanity.com/paper/2604.10123/) (2026)
- [Speech Recognition-based Feature Extraction for Enhanced Automatic Severity Classification in Dysarthric Speech](https://mlsanity.com/paper/2412.03784/) (2024)
- [Classification of Dysarthria based on the Levels of Severity. A Systematic Review](https://mlsanity.com/paper/2310.07264/) (2023)
- [Cross-lingual Retrieval-Augmented Classification for Dysarthria Severity Assessment](https://mlsanity.com/paper/2606.22910/) (2026)

## Abstract (arXiv)

This paper proposes a cross-lingual classification method for English, Korean, and Tamil, which employs both language-independent features and language-unique features. First, we extract thirty-nine features from diverse speech dimensions such as voice quality, pronunciation, and prosody. Second, feature selections are applied to identify the optimal feature set for each language. A set of shared features and a set of distinctive features are distinguished by comparing the feature selection results of the three languages. Lastly, automatic severity classification is performed, utilizing the two feature sets. Notably, the proposed method removes different features by languages to prevent the negative effect of unique features for other languages. Accordingly, eXtreme Gradient Boosting (XGBoost) algorithm is employed for classification, due to its strength in imputing missing data. In order to validate the effectiveness of our proposed method, two baseline experiments are conducted: experiments using the intersection set of mono-lingual feature sets (Intersection) and experiments using the union set of mono-lingual feature sets (Union). According to the experimental results, our method achieves better performance with a 67.14% F1 score, compared to 64.52% for the Intersection experiment and 66.74% for the Union experiment. Further, the proposed method attains better performances than mono-lingual classifications for all three languages, achieving 17.67%, 2.28%, 7.79% relative percentage increases for English, Korean, and Tamil, respectively. The result specifies that commonly shared features and language-specific features must be considered separately for cross-language dysarthria severity classification.