This study focuses on determining a procedure to select effective negative examples for development of improved Support Vector Machine (SVM) based speaker recognition. Selection o...
Jun-Won Suh, Yun Lei, Wooil Kim, John H. L. Hansen
: Speaker recognition in applications of our daily lives is not yet in widespread use. In order for biometric technology to make sense for real-world authentication applications an...
—Discriminative Training (DT) methods for acoustic modeling, such as MMI, MCE, and SVM, have been proved effective in speaker recognition. In this paper we propose a DT method fo...
The Gaussian Mixture Model (GMM) is often used in conjunction with Mel-frequency cepstral coefficient (MFCC) feature vectors for speaker recognition. A great challenge is to use ...
Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM)...