# Synthetic Speech Augmentation

Automated screening for voice disorders is limited by scarce, imbalanced clinical speech data. Using standardized sentence recordings from the Saarbrücken Voice Database, two text-to-speech models each generated one synthetic sample per training recording, and several architectures and fusion strategies were compared. In blinded listening only 52.6% of Kartoffelbox samples were identified as synthetic versus 89.5% for XTTS; with Kartoffelbox augmentation HuBERT reached 0.88 sensitivity and 0.87 accuracy, while laryngitis remained hardest in the four-class task.

Automated screening for voice disorders is limited by scarce, imbalanced clinical speech data. Using standardized sentence recordings from the Saarbrücken Voice Database, two text-to-speech models each generated one synthetic sample per training recording, and several architectures and fusion strategies were compared. In blinded listening only 52.6% of Kartoffelbox samples were identified as synthetic versus 89.5% for XTTS; with Kartoffelbox augmentation HuBERT reached 0.88 sensitivity and 0.87 accuracy, while laryngitis remained hardest in the four-class task.