3. A method comprising the steps of: determining a cluster for a unit to be subjected to text-to-speech conversion; determining the Gaussian Mixture Model for the cluster, wherein the Gaussian Mixture Model is generated for a sample clustered through the use of a decision tree which includes combining two branches in the decision tree for clustering samples if the two branches are similar for further clustering; calculating the distance between candidate samples in the cluster and the determined Gaussian Mixture Model; using electronic logic circuitry to identify the sample with the smallest distance for subsequent speech synthesizing; and transforming the identified sample into synthesized speech.
10. The method according to claim 3 , wherein the steps of calculating the distance between the candidate samples in the cluster and the determined Gaussian Mixture Model and identifying the sample with the smallest distance for subsequent speech synthesizing comprises: obtaining descriptive prosody annotation information of each unit to be subjected to text-to-speech conversion; finding the context equivalent cluster of each unit to be subjected to text-to-speech conversion, the cluster coffesponding to a Gaussian Mixture Model; evaluating all the candidates of the unit to be text-to-speech conversion synthesized through the Gaussian Mixture Model-based distance definition; and finding the overall optimal candidate series, for subsequent speech synthesizing, based on the distance given in the evaluating step and criteria of overall optimization.