Scaling Under-Resourced TTS: A Data-Optimized Framework with Advanced Acoustic Modeling for Thai

Text-to-speech (TTS) technology has achieved impressive results for widely spoken languages, yet many under-resourced languages remain challenged by limited data and linguistic complexities.In this paper, we present a novel methodology that integrates a data-optimized framework with an advanced acoustic model to build high-quality TTS systems for lowresource scenarios.We demonstrate the effectiveness of our approach using Thai as an illustrative case, where intricate phonetic rules and sparse resources are effectively addressed.Our method enables zero-shot voice cloning and improved performance across diverse client applications, ranging from finance to healthcare, education, and law.Extensive evaluations-both subjective and objective-confirm that our model meets state-of-the-art standards, offering a scalable solution for TTS production in datalimited settings, with significant implications for broader industry adoption and multilingual accessibility.