Discriminative Classifiers For Speaker Recognition. This item is unavailable.Discriminative Classifiers For Speaker Recognition

Due to the growing need for security applications,speaker recognition as the biometric task ofauthenticating a claimant by voice has currentlybecome a focus of interest.In this book we present new approaches to integratediscriminative classifiers like Support VectorMachines (SVMs) and Sparse Kernel Logistic Regression(SKLR) into speaker recognition systems that aretraditionally based on generative classifiers likeGaussian Mixture Models (GMMs).In a first approach for limited training data thediscriminative classifiers are applied directly onfeature vectors from parameterized speech frames andit is shown that both, SVM as well as SKLR outperformtraditional methods.In the second approach a state-of-the-art speakerrecognition system for large amount of training datais designed that combines Gaussian Mixture Modelswith discriminative classifiers.Furthermore, we investigate different featureextraction methods for speaker recognition on largeamount of training data and it is shown that theapplication of fusion schemes that combine thesesubsystems yield a significant improvement of therecognition performance in comparison to theapplication of single subsystems.

Marcel Katz studied Electrical Engineering in Duesseldorf andreceived his PhD for his works in the fields of speech andspeaker recognition in 2008 from the University of Magdeburg,Germany. He successfully participated in several speakerrecognition evaluations and is currently working as a speechrecognition specialist in Cambridge, UK.