This paper addresses the challenges of data scarcity and limited speaker resources in Lao-English code-switched speech synthesis. We propose a neural encoder-decoder-based method for mixed-lingual speech synthesis. The method first extracts phoneme-level speech representations and employs a dot-product attention mechanism to map Lao and English phonemes into a shared latent space, thereby enhancing the model’s capability to represent cross-lingual phonetic information. In addition, language ID embedding module is extended to explicitly indicate the language of each input token, helping the model distinguish and adapt to language-specific pronunciation characteristics. Experiments are conducted on the open-source English dataset LibriTTS and a proprietary Lao speech corpus. Both subjective evaluations (MOS, AB preference tests) and objective metrics (RMSE) demonstrate that the proposed approach significantly outperforms the baseline VALL-E X model in terms of naturalness and language-switching fluency. Furthermore, ablation studies confirm that both the shared phoneme latent space and the language ID module play critical roles in improving synthesis quality. This approach offers a novel solution for integrating low-resource languages into mixed-lingual speech synthesis.