7. A machine-readable non-transitory storage medium having instructions to cause a machine to perform 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, 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, and wherein the redundancy pruning is performed on a representation of voice units, the representation being stored in a memory of a data processing system which includes a processor which performs the pruning.
12. The machine-readable medium of claim 7 wherein the feature vectors representing the instances are created by matrix-style modal analysis via singular value decomposition of a matrix W, wherein the matrix W is an M×N matrix where M is the number of instances, N is the maximum number of segment samples corresponding to an instance, with the matrix W being zero padded to N samples, wherein the singular value decomposition is represented by 
 W=USV T where U is the M×R left singular matrix with row vectors u i (1≦i≦M), S is the R×R diagonal matrix of singular values s 1 ≧s 2 ≧ . . . ≧s R >0, V is the N×R right singular matrix with row vectors v j (1≦j≦N), R≦min (M, N), and T denotes matrix transposition, wherein the feature vector ū i is calculated as 
 ū i =u i S where u i is a row vector associated with an instance i, and S is the singular diagonal matrix, and wherein the distance between two feature vectors is determined by a metric comprising a similarity measure, C, between two feature vectors, ū i and ū j , wherein C is calculated as C ⁡ ( u _ i , u _ j ) = cos ⁡ ( u i ⁢ S , u j ⁢ S ) = u i ⁢ S 2 ⁢ u j T  u i ⁢ S  ⁢  u j ⁢ S  for any 1≦i, j≦M.