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.
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. 9. The method according to claim 3 , wherein the step of determining the cluster for the unit to be subjected to text-to-speech conversion 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; and in the space of the Gaussian Mixture Model mixture model sequence, searching for the best values based on the distance definition and criteria of overall optimization.