21. The computer readable medium of claim 19 , wherein forming the plurality of voice waveforms includes forming the plurality of voice waveforms in a same format as the voice signal, and wherein the act of selecting the at least one of the plurality of candidate feature sets includes an act of selecting at least one of the plurality of candidate feature sets corresponding to a respective at least one of the plurality of voice waveforms that is most similar to the voice signal according to a first criteria, the selected one of the plurality of candidate feature sets being used to train, at least in part, the voice synthesis model.
19. A computer readable medium encoded with a speech synthesis model for use with a formant-based text-to-speech synthesizer adapted to, when operating, generate human recognizable speech, the speech synthesis model trained to generate the human recognizable speech, at least in part, by performing acts of: detecting a plurality of candidate features in the voice signal; grouping different combinations of the plurality of candidate features into a plurality of candidate feature sets; forming a plurality of voice waveforms, each of the plurality of voice waveforms formed, at least in part, by processing a respective one of the plurality of candidate feature sets; performing at least one comparison between the voice signal and each of the plurality of voice waveforms; selecting at least one of the plurality of candidate feature sets based, at least in part, on the at least one comparison with the voice signal; and using the selected at least one of the plurality of candidate feature sets to assist in training the speech synthesis model by incorporating and/or modifying at least one rule in the speech synthesis model, the at least one rule specifying how features should transition over time when synthesizing speech from a given text, wherein the speech synthesis model, when trained, is configured to synthesize the speech from the given text without using pre-recorded voice fragments.