This paper proposes a decision-tree backing-off technique for an HMM-based speech synthesis system. In the system, a decision-tree based context clustering technique is used for constructing parameter tying structures. In the context clustering, the MDL criterion has been used as a stopping criterion. In this paper, however, huge decision-trees are constructed without any stopping criterion. In the synthesis phase, decision-trees obtained in this way are used in the proposed backing-off scheme. This enables us to adjust the cluster size dynamically at run-time according to the text to be synthesized. Results of subjective listening tests show that the proposed technique improves the synthesized speech quality.