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This paper proposes a cross-lingual classification method for English,Korean, and Tamil, which employs both language-independent features andlanguage-unique features. First, we extract thirty-nine features from diversespeech dimensions such as voice quality, pronunciation, and prosody. Second,feature selections are applied to identify the optimal feature set for eachlanguage. A set of shared features and a set of distinctive features aredistinguished by comparing the feature selection results of the threelanguages. Lastly, automatic severity classification is performed, utilizingthe two feature sets. Notably, the proposed method removes different featuresby languages to prevent the negative effect of unique features for otherlanguages. Accordingly, eXtreme Gradient Boosting (XGBoost) algorithm isemployed for classification, due to its strength in imputing missing data. Inorder to validate the effectiveness of our proposed method, two baselineexperiments are conducted: experiments using the intersection set ofmono-lingual feature sets (Intersection) and experiments using the union set ofmono-lingual feature sets (Union). According to the experimental results, ourmethod 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-lingualclassifications for all three languages, achieving 17.67%, 2.28%, 7.79%relative percentage increases for English, Korean, and Tamil, respectively. Theresult specifies that commonly shared features and language-specific featuresmust be considered separately for cross-language dysarthria severityclassification.

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