29. A text-to-speech synthesis system comprising a redundancy pruned voice table, wherein the voice table is pruned from an original voice table according to a machine-implemented method comprising: pruning redundancy of instances in the original voice table, 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.
29. A text-to-speech synthesis system comprising a redundancy pruned voice table, wherein the voice table is pruned from an original voice table according to a machine-implemented method comprising: pruning redundancy of instances in the original voice table, 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. 32. The text-to-speech synthesis system of claim 29 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.