Data Augmentation creates additional training examples by modifying existing audio or text data. Common techniques include adding background noise, changing speed, adjusting pitch, or generating synthetic speech.
Voice AI developers use Data Augmentation to improve model robustness across accents, environments, and speaking styles. It helps reduce overfitting while increasing recognition accuracy in real-world conditions.