Voice Disorder Classification Using Wav2vec 2.0 Feature Extraction.

Abstract

Objectives: The study aims to classify normal and pathological voices by leveraging the wav2vec 2.0 model as a feature extraction method in conjunction with machine learning classifiers.

Methods: Voice recordings were sourced from the publicly accessible VOICED database. The data underwent preprocessing, including normalization and data augmentation, before being input into the wav2vec 2.0 model for feature extraction. The extracted features were then used to train four machine learning models-Support Vector Machine (SVM), K-Nearest Neighbors, Decision Tree (DT), and Random Forest (RF)-which were evaluated using Stratified K-Fold cross-validation. Performance metrics such as accuracy, precision, recall, F1-score, macro average, micro average, receiver-operating characteristic (ROC) curve, and confusion matrix were utilized to assess model performance.

Results: The RF model achieved the highest accuracy (0.98 ± 0.02), alongside strong recall (0.97 ± 0.04), F1-score (0.95 ± 0.05), and consistently high area under the curve (AUC) values approaching 1.00, indicating superior classification performance. The DT model also demonstrated excellent performance, particularly in precision (0.97 ± 0.02) and F1-score (0.96 ± 0.02), with AUC values ranging from 0.86 to 1.00. Macro-averaged and micro-averaged analyses showed that the DT model provided the most balanced and consistent performance across all classes, while RF model exhibited robust performance across multiple metrics. Additionally, data augmentation significantly enhanced the performance of all models, with marked improvements in accuracy, recall, F1-score, and AUC values, especially notable in the RF and DT models. ROC curve analysis further confirms the consistency and reliability of the RF and SVM models across different folds, while confusion matrix analysis revealed that RF and SVM models had the fewest misclassifications in distinguishing "Normal" and "Pathological" samples. Consequently, RF and DT models emerged as the most robust performers, making them particularly well-suited for the voice classification task in this study.

Conclusions: The method of wav2vec 2.0 combining machine learning models proved highly effective in classifying normal and pathological voices, achieving exceptional accuracy and robustness across various machine evaluation metrics.