This paper demonstrates that modelling session variability during GMM training can improve the performance of a GMM supervector SVM speaker verification system. Recently, a method of modelling session variability in GMM-UBM systems has led to significant improvements when the training and testing conditions are subject to session effects. In this work, session variability modelling is applied d...

We describe a GMM-UBM-based evaluation system for pathologic voices that uses standard cepstral features. Per speaker one GMM is created and its components are used to create a so-called GMM supervector. The supervector of each speaker is labeled with the intelligibility values obtained by human evaluation and is used to train an SVR. We studied different GMM supervectors containing different G...

Variability in speech due to dialect is a major factor limiting speech system performance for speech recognition, spoken document retrieval, and dialog systems. In this study, we propose a novel discriminative algorithm to improve dialect classification for unsupervised spontaneous speech in Arabic. No transcripts are used for either training or testing, and all data are spontaneous speech. The...

This paper presents a novel automatic speaker age and gender identification approach which combines five different methods at the acoustic level to improve the baseline performance. The five subsystems are (1) Gaussian mixture model (GMM) system based on mel-frequency cepstral coefficient (MFCC) features, (2) Support vector machine (SVM) based on GMM mean supervectors, (3) SVM based on GMM maxi...