Deep Learning Classification of Multicategory Voice Disorders Using Multi-Vowel Mel Spectrograms
Yimiao Wang1, Shuaichi Ma1,2, Chen Zhao1,2
1ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, People's Republic of China.
Background:
Voice disorders caused by vocal fold lesions or mobility disorders are common, but current diagnosis depends on mildly invasive laryngoscopy.
Objective:
To develop and validate a deep learning model for automatic classification of multiple voice disorder categories using non-invasive voice recordings.
Methods:
We retrospectively analyzed 897 patients and healthy controls from a tertiary ENT center. Sustained Mandarin vowels (/a/, /e/, /o/, /i/, /u/, /v/) were recorded and converted to Mel spectrograms. A ResNet and feature pyramid network-based model was trained with weighted cross-entropy loss and evaluated on 3-class (normal, laryngeal lesions, vocal fold mobility disorders) and binary (high-risk vs benign laryngeal lesions) tasks.
Results:
For the 3-class task, the model achieved accuracy 0.795, sensitivity 0.818, specificity 0.900, precision 0.786, and F1 score 0.794. For the binary task, performance improved to accuracy 0.826, sensitivity 0.933, specificity 0.625, precision 0.824, and F1 score 0.876, indicating high sensitivity for detecting high-risk laryngeal lesions.
Conclusion:
A deep learning model using multi-vowel Mel spectrograms can non-invasively classify common voice disorders with clinically meaningful accuracy and shows particular promise as a screening tool for high-risk laryngeal lesions.
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