Synthetic Data for Text-to-Speech: A Study of Feasibility and Generalization

This research explores the use of synthetic data for training text-to-speech models, which could significantly reduce the need for large, manually-labeled datasets. Understanding the feasibility and generalization capabilities of models trained on synthetic data is crucial for future advancements in speech synthesis.

• •Investigates the potential of synthetic data for text-to-speech model training.
• •Examines the sensitivity of these models to the characteristics of the synthetic data.
• •Assesses the generalization capabilities of the trained models.