Hierarchical AI classification of benign laryngeal disorders using acoustic voice features

Benign laryngeal voice disorders affect nearly one in five individuals and often manifest as dysphonia, while also serving as non-invasive indicators of broader physiological dysfunction. We propose a clinically inspired hierarchical machine learning framework for automated classification of eight benign voice disorders alongside healthy controls using acoustic features extracted from short, sustained vowel phonations. Experiments were conducted on 15,132 recordings from 1,261 speakers in the Saarbrücken Voice Database, covering vowels /a/, /i/, and /u/ produced at neutral, high, low, and gliding pitches. Mimicking clinical triage workflows, the framework operates in three sequential stages: Stage 1 performs binary pathological screening by integrating convolutional neural network–derived mel-spectrogram representations with 21 interpretable acoustic features; Stage 2 groups voices into Healthy, Functional/Psychogenic, and Structural/Inflammatory categories; and Stage 3 performs fine-grained subtype classification by incorporating probabilistic outputs from preceding stages as auxiliary diagnostic features. The hierarchical framework demonstrated improved performance compared to flat multiclass classifiers and general-purpose self-supervised models (HuBERT, HeAR), suggesting the value of hierarchical decomposition for complex voice-disorder classification. A confidence-aware human-in-the-loop strategy further improved diagnostic performance by selectively referring low-confidence predictions for expert review while maintaining substantial automatic coverage, providing a practical pathway toward trustworthy clinical deployment and continual model refinement. But external validation on independent cohorts remains essential before clinical deployment. By combining deep spectral learning with interpretable voice features, the proposed framework demonstrates potential as a component of comprehensive diagnostic assessment pending clinical validation and external testing on diverse populations.