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Identification of non-native personnel is a critical piece of information for making crucial on-the-spot decisions for security purposes. Identification of a non-native speaker is often readily apparent in normal conversation with a native speaker through speech content and accent. Such identification which requires familiarity with language nuances may not be possible for a non-native interrogator or intelligence analyst or when conversing or listening through a machine language translator. Developing an automatic system to identify speakers as native or non-native, as well as their native language, including dialect, within input audio streams, is the major goal of this project. Such a system may be used alone or with other downstream applications such as machine language translation systems. In this paper we present four approaches to identify native and non-native speakers as a binary recognition problem. The approaches can be further categorized into phonetic-based approaches and non-phonetic-based approaches. These approaches were tested on two separate databases, including text-dependent read speech and text-independent spontaneous speech. The results show that our system is competitive in comparison with other published, state-of-the-art non-native speaker recognition systems. Key metrics for automated non-native recognition systems include: 1) positive identification rates, 2) false alarm/identification rates, and 3) length of captured speech sample required to reach a decision.