Forensic speaker identification based on spectral moments

A new method for doing text-independent speaker identification geared to forensic situations is presented. By analysing 'isolexemic' sequences, the method addresses the issues of very short criminal exemplars and the need for open-set identification. An algorithm is given that computes an average spectral shape of the speech to be analysed for each glottal pulse period. Each such spectrum is converted to a probability density function and the first moment (i.e. the mean) and the second moment about the mean (i.e. the variance) are computed. Sequences of moment values are used as the basis for extracting variables that discriminate among speakers. Ten variables are presented all of which have sufficiently high inter- to intraspeaker variation to be effective discriminators. A case study comprising a ten-speaker database, and ten unknown speakers, is presented. A discriminant analysis is performed and the statistical measurements that result suggest that the method is potentially effective. The report represents work in progress.