33. A machine-implemented method comprising: identifying instances in a plurality of speech segments; creating feature vectors derived from a machine perception transformation of time-domain samples corresponding to the instances in the plurality of speech segments onto a feature space, 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; clustering the feature vectors using a similarity measure in the feature space; and replacing the clustered instances corresponding to the clustered feature vectors within a radius by a single instance.
33. A machine-implemented method comprising: identifying instances in a plurality of speech segments; creating feature vectors derived from a machine perception transformation of time-domain samples corresponding to the instances in the plurality of speech segments onto a feature space, 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; clustering the feature vectors using a similarity measure in the feature space; and replacing the clustered instances corresponding to the clustered feature vectors within a radius by a single instance. 38. The machine-implemented method of claim 33 wherein the cluster radius is controlled by a user.