Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech

Researchers studied a third route for deploying text-to-speech in low-resource settings by using a large voice-cloning model as a programmable data source to generate synthetic speech for building a compact fixed-voice Thai TTS system, avoiding both costly inference of large voice-cloning models and the need for a speaker-specific corpus. The work targets Thai, a low-resource language, and frames the approach as a way to turn a small amount of data into a fixed-voice system.

In low-resource settings, deploying TTS typically requires choosing between a large voice-cloning model with costly inference or a compact fixed-voice system that requires a speaker-specific corpus. We study a third route: using a large voice-cloning model as a programmable data source to turn a sho