In Thai language, stress is an important prosodic feature that not only affects naturalness but also has a crucial role in meaning of phrase-level utterance. It is seen that a speech synthesis model that is trained with lack of stress and phrase-level information causes incorrect tones and ambiguity in meaning of synthetic speech. Our previous work has shown that manually annotated stress information improves naturalness of synthetic speech. However, a high time consumption is a drawback of the manual annotation. In this paper, we utilize an unsupervised learning technique called Bayesian Gaussian process latent variable model (Bayesian GP-LVM) to automatically put stress annotation on the given training data. Stress related features are projected onto a latent space in which syllables are easier classified into stressed/unstressed classes. We use the stressed/unstressed information as an additional context in GPR-based speech synthesis. Experimental results show that the proposed technique improves naturalness of synthetic speech as well as accuracy of stressed/unstressed classification. Moreover, the proposed technique enables us to avoid ambiguity in meaning of synthetic speech by providing intended stress position into context label sequence to be synthesized.