Speaker Recognition from Coded Speech Using Support Vector Machines

We proposed to use support vector machines (SVMs) to recognize speakers from signal transcoded with different speech codecs. Experiments with SVM-based text-independent speaker classification using a linear GMM supervector kernel were presented for six different codecs and uncoded speech. Both matched (the same codec for creating speaker models and for testing) and mismatched conditions were investigated. SVMs proved to provide high accuracy of speaker recognition, however requiring higher number of Gaussian mixtures than in the baseline GMM-UBM system. In mismatched conditions the Speex codec was shown to perform best for creating robust speaker models.