A Smoothing Kernel for Spatially Related Features and Its Application to Speaker Verification

LUCIANA FERRER; KEMAL SÖNMEZ; ELIZABETH SHRIBERG

Antwerp

Congreso; Interspeech Conference; 2007

ISCA

Most commonly used kernels are invariant to permutations of the feature vector components. This characteristic may make machine learning methods that use such kernels suboptimal in cases where the feature vector has an underlying structure. In this paper we will consider one such case, where the features are spatially related. We show a way to modify the objective function of the support vector machine (SVM) optimization problem to account for this structure. The new optimization problem can be implemented as a standard SVM using a particular smoothing kernel. Results are shown on a speaker verification task using prosodic features that are transformed using a particular implementation of the Fisher score. The proposed method leads to improvements of as much as 15% in equal error rate (EER).