September 22-25, 1997
In this paper we present a new approach to text independent speaker verification. Speaker models are created from complete data sets, derived from a set of sentences. A decision on an identity claim is based on the calculation of the mean next neighbour distance between a speaker model and a test utterance. A Vector quantization technique serves to efficiently extract this frame based similarity measure. It is the purpose of this paper to investigate this new approach and test its performance on a large database as a function of a number of parameters, i.e., the number of data vectors in each model and the length of the test utterance. The best results on a set of 108 speakers are 0:93% false rejection rate and 0:98% false acceptance rate.
Bibliographic reference. Dersch, Dominik R. / King, Robin W. (1997): "Speaker models designed from complete data sets: a new approach to text-independent speaker verification", In EUROSPEECH-1997, 2283-2286.