On the fusion of dissimilarity-based classifiers for speaker identification

In this work, we describe a speaker identification system that uses multiple supplementary information sources for computing a combined match score for the unknown speaker. Each speaker profile in the database consists of multiple feature vector sets that can vary in their scale, dimensionality, and the number of vectors. The evidence from a given feature set is weighted by its reliability that is set in a priori fashion. The confidence of the identification result is also estimated. The system is evaluated with a corpus of 110 Finnish speakers. The evaluated feature sets include mel-cepstrum, LPC-cepstrum, dynamic cepstrum, long-term averaged spectrum of /A/ vowel, and F0.

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