Large pretrained text-to-speech (TTS) models sound almost human for well-resourced languages, but much worse for languages that are rare in their training data. We study this quality gap for Khmer and Korean using VoxCPM2, a 2.4B parameter, tokenizer-free TTS model that joins a MiniCPM-4 language-model backbone with a flow-matching diffusion decoder. We build one shared, language-tagged corpus of 25.5 hours after cleaning and adapt VoxCPM2 with a single Low-Rank Adaptation (LoRA) adapter, trained on both languages at once and added to both the language model and the decoder. The adapter is zero-initialized, so training starts exactly at the original zero-shot model. In native-speaker listening tests, the Khmer Mean Opinion Score (MOS) rises from 3.85 to 4.23 with the best adapter, rank 64. This gain is highly significant under a paired Wilcoxon test with p < 0.001, and it is achieved while training only 0.19 to 3.03 percent of the parameters. Two findings stand out. First, the training loss and human ratings disagree on the best rank. The loss is lowest at rank 128, but MOS peaks at rank 64. Second, the same adapter gives no significant gain for Korean, which the base model already covers well, and a high rank even hurts quality. This shows that adaptation helps mainly where the base model is truly weak.

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