4. The computer readable medium of claim 1 , wherein the generating comprises: performing pre-selection to compose a rich context model candidate sausage for the input text, the candidate sausage including a plurality of refined rich context model sequences, each sequence including at least some refined rich context models from the plurality of refined rich context models; selecting one of the plurality of refined rich context model sequences that has a least divergence from a guiding sequence that is obtained from the decision tree-tied HMMs; and generating output speech for the input text based at least on a rich context model sequence that is selected from the plurality of refined rich context model sequences.
4. The computer readable medium of claim 1 , wherein the generating comprises: performing pre-selection to compose a rich context model candidate sausage for the input text, the candidate sausage including a plurality of refined rich context model sequences, each sequence including at least some refined rich context models from the plurality of refined rich context models; selecting one of the plurality of refined rich context model sequences that has a least divergence from a guiding sequence that is obtained from the decision tree-tied HMMs; and generating output speech for the input text based at least on a rich context model sequence that is selected from the plurality of refined rich context model sequences. 7. The computer readable medium of claim 4 , wherein the generating further includes synthesizing speech based further on line spectral pair (LSP) coefficients, a fundamental frequency, and a gain predicted from the input text.