A Speaker Verification System Using SVM over a Spanish Corpus

Abstract

This paper presents a description of the principal aspects employed in the development of a speaker verification system based on a Spanish corpus. The main goal is to obtain classification results and behavior using Support Vector Machines (SVM) as the classifier technique. The most relevant aspects involved in developing a Spanish corpus are given. For the front end processing a novel method to suppress silences between words is proposed and successfully applied. The validation to the complete system is made using randomly selected feature vectors and vectors from continuous sequences of the voice signal. Additionally, Gaussian Mixtures Models (GMM) and Artificial Neural Networks (ANN) are also used as classifiers to compare and validate the results.