Presentation Type
Article
Location
Kennesaw, Georgia
Start Date
1-4-2026 3:00 PM
End Date
1-4-2026 4:15 PM
Description
Voice has garnered significant interest as a biomarker for diagnosing and managing neurological, respiratory, and cardiovascular diseases. However, upper airway surgeries such as septoplasty, tonsillectomy, and functional endoscopic sinus surgery (FESS) can substantially alter vocal tract anatomy, thereby changing vocal acoustics and potentially confounding voice-based diagnostic systems. This study investigates whether acoustic features from sustained vowels can differentiate between these procedures and a control group. Audio was collected from 105 Spanish-speaking participants 15 days pre surgery and 15 days post surgery. For each vowel, 121 chroma, cepstral, spectral, and temporal features were extracted and then reduced to 90 longitudinally responsive features using mixed repeated-measures ANOVA and post-hoc pairwise contrasts. Ten supervised classifiers (Random Forest, Logistic Regression, Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, SVM with linear and RBF kernels, LightGBM, XGBoost, CatBoost) were evaluated for Control vs. Surgery at 15 days post-op, using stratified 5-fold cross-validation with F1 score as the primary metric. At 15 days post-surgery, classification of control versus surgical patients achieved F1 scores in the high-60% to low-70% range for the best models, with LightGBM, CatBoost, and KNN providing the strongest overall performance. Model interpretability using SHapley Additive exPlanations indicated that this early discrimination was driven by a compact set of vowel-based features, particularly MFCC statistics, higher-order delta/delta-squared coefficients, and chroma intensity for specific pitch classes. These results provide proof of concept that surgery-aware voice analytics can detect early post-operative status from sustained vowels and underscore the value of incorporating surgical history and recovery stage into future voice-based monitoring systems.
Voice as a Bio-Marker of Surgical Recovery: Classifying Post-Operative ENT Patients Using Vowel Acoustics
Kennesaw, Georgia
Voice has garnered significant interest as a biomarker for diagnosing and managing neurological, respiratory, and cardiovascular diseases. However, upper airway surgeries such as septoplasty, tonsillectomy, and functional endoscopic sinus surgery (FESS) can substantially alter vocal tract anatomy, thereby changing vocal acoustics and potentially confounding voice-based diagnostic systems. This study investigates whether acoustic features from sustained vowels can differentiate between these procedures and a control group. Audio was collected from 105 Spanish-speaking participants 15 days pre surgery and 15 days post surgery. For each vowel, 121 chroma, cepstral, spectral, and temporal features were extracted and then reduced to 90 longitudinally responsive features using mixed repeated-measures ANOVA and post-hoc pairwise contrasts. Ten supervised classifiers (Random Forest, Logistic Regression, Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, SVM with linear and RBF kernels, LightGBM, XGBoost, CatBoost) were evaluated for Control vs. Surgery at 15 days post-op, using stratified 5-fold cross-validation with F1 score as the primary metric. At 15 days post-surgery, classification of control versus surgical patients achieved F1 scores in the high-60% to low-70% range for the best models, with LightGBM, CatBoost, and KNN providing the strongest overall performance. Model interpretability using SHapley Additive exPlanations indicated that this early discrimination was driven by a compact set of vowel-based features, particularly MFCC statistics, higher-order delta/delta-squared coefficients, and chroma intensity for specific pitch classes. These results provide proof of concept that surgery-aware voice analytics can detect early post-operative status from sustained vowels and underscore the value of incorporating surgical history and recovery stage into future voice-based monitoring systems.
