Performance analysis of speaker features extracted from high-order fractional domains

An evaluation of the feature set of the vector difference (VD) based on fractional cosine and sine transform focusing on the high-order fractional domains for text-independent speaker recognition is elucidated in this paper. The experiments have been done following the principles varying the number of the vector dimension and the power of the output parameters of fractional cosine and sine transform separately. The recognition results show that when the order of primary fractional domain is fixed to be 1 and the one of secondary fractional domain is 0.98, the correct recognition rate of the VD feature matches the one of the previous MFCC feature