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 low-resource 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 data-limited settings, with significant implications for broader industry adoption and multilingual accessibility. All demos are available in https://luoji.cn/static/thai/demo.html.

We consider the problem of identifying a minimal subset of training data 𝒮_t such that if the instances comprising 𝒮_t had been removed prior to training, the categorization of a given test point x_t would have been different.Identifying such a set may be of interest for a few reasons.First, the cardinality of 𝒮_t provides a measure of robustness (if |𝒮_t| is small for x_t, we might be less confident in the corresponding prediction), which we show is correlated with but complementary to predicted probabilities.Second, interrogation of 𝒮_t may provide a novel mechanism for contesting a particular model prediction: If one can make the case that the points in 𝒮_t are wrongly labeled or irrelevant, this may argue for overturning the associated prediction. Identifying 𝒮_t via brute-force is intractable.We propose comparatively fast approximation methods to find 𝒮_t based on influence functions, and find that—for simple convex text classification models—these approaches can often successfully identify relatively small sets of training examples which, if removed, would flip the prediction.