Synthetic Data for Text-to-Speech: A Study of Feasibility and Generalization
Analysis
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.
Key Takeaways
- •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.
Reference / Citation
View Original"The study focuses on the feasibility, sensitivity, and generalization capability of models trained on purely synthetic data."