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.

提出了一种跨语言分类方法，使用语言独立特征和语言唯一特征进行重音障碍严重性分类，并使用 XGBoost 算法进行分类。