Factor analysis-based information integration for Arabic dialect identification

In this study, we propose a new factor analysis-based modeling technique to more clearly describe the composition of the supervector defined by the GMM model for dialect identification. The method utilizes knowledge types of information contained in the transcript file of the data. We evaluate the effects of the proposed modeling algorithm on a GMM-based Arabic dialect identification system. In particular, we compare eigenchannel modeling and our proposed information integration modeling. We show that the proposed modeling can obtain a 4.23% relative EER reduction with the same total number of factors, and a 9.37% relative EER reduction with the same number of channel/session factors versus eigenchannel modeling.