Text independent speaker recognition using orthogonal linear prediction

Recent experiments in speech synthesis have shown, that by an appropriate eigenvector analysis, a set of orthogonal parameters can be obtained that is essentially independent of all linguistic information across an analyzed utterance but highly indicative of the identity of the speaker. The orthogonal parameters are formed by a linear transformation of the linear prediction parameters, and can achieve their recognition potential without the need of any time normalization procedure. The speaker discrimination potential of the linear prediction orthogonal parameters were formally tested in both a speaker recognition and a speaker verification experiment. The speech data for these experiments consisted of six repetitions of the same sentence spoken by 21 male speakers on six separate occasions. For both recognition and verification, the identification accuracy of the orthogonal parameters exceeded 99%. In a separate text-independent speaker recognition experiment, an accuracy of 94% was achieved.