Speech contains valuable information regarding the traits of speakers. This paper investigates two aspects of this information. The first is automatic detection of non-native speakers and their native language on relatively large data sets. We present several experiments which show how our system outperforms the best published results on both the Fisher database and the foreign-accented English (FAE) database for detecting non-native speakers and their native language respectively. Such performance is achieved by using an SVM-based classifier with ASR-based features integrated with a novel universal background model (UBM) obtained by clustering the Gaussian components of an ASR acoustic model. The second aspect of this work is to utilize the detected speaker characteristics within a speaker recognition system to improve its performance. ©2010 IEEE.