2. The machine-implemented method of claim 1 wherein the instances are the instances of a phoneme, a diphone, a syllable, a word, or a sequence unit and wherein a first set of the instances subjected to redundancy pruning are clustered with a first feature vector and a second set of the instances subjected to redundancy pruning are clustered with a second feature vector that is discernably separated from the first feature vector.
1. A machine-implemented method comprising: pruning redundancy of instances in a plurality of speech segments, wherein the redundancy criterion is based on a similarity measure between feature vectors derived from a machine perception transformation of time-domain samples corresponding to the instances in the plurality of speech segments, wherein the instances subjected to redundancy pruning are clustered together with feature vectors discernably separated from each other in the machine perception transformation and wherein the machine perception transformation is correlated with human perception by using the time-domain samples retaining both amplitude and phase information of the speech segments, which were provided in sound data for a speech synthesis system.