This study presents an approach to Hidden Markov Models (HMM)-based spontaneous speech synthesis with pronunciation variation for better spontaneity. Pronunciation variation generally occurs in spontaneous speech and plays an important role in expressing the spontaneity. In this study, a state-based transformation function is adopted to model the relation between read speech and the corresponding spontaneous speech with pronunciation variations. The transformation function is then used to generate the state-based pronunciation variations. Due to the lack of training data, the articulatory features are used to cluster the transformation functions using Classification and Regression Trees (CARTs) such that the unseen pronunciation variation with the same articulatory features can be generated from the transformation function in the same cluster. Objective and subjective tests are conducted to evaluate the performance of the proposed approach. The experimental results show that the proposed transformation function achieves a significant improvement on spontaneity in synthesized speech.