A Study on Neural-Network-Based Text-to-Speech Adaptation Techniques for Vietnamese

One of the main goals of text-to-speech adaptation techniques is to produce a model that can generate good quality audio given a small amount of training data. In fact, TTS systems for rich-resource languages have good quality because of a large amount of data, but training models with small datasets (or low-resources) is not an easy task, which often produces low-quality sounds. One of the approaches to overcome the data limitation is fine-tuning. However, we still need a pre-trained model which learns from large amount of data in advance. The paper presents two contributions: (1) a study on the amounts of data needed for a traditional fine-tuning method for Vietnamese, where we change the data and run the training for a few more iterations; (2) we present a new fine-tuning pipeline which allows us to borrow a pre-trained model from English and adapt it to any Vietnamese voices with a very small amount of data while still maintaining a good speech synthetic sound. Our experiments show that with only 4 minutes of data, we can synthesize a new voice with a good similarity score, and with 16 minutes of data, the model can generate audio with a 3.8 MOS score.