Recent gains in the performance of automatic speaker recognition systems have been obtained by new methods in subspace modeling. This talk presents the development of speaker recognition systems ranging from traditional approaches, such as Gaussian mixture modeling (GMM) to novel state-of-the-art systems employing subspace techniques, such as factor analysis and iVector methods. This seminar also covers research on the means to exploit high-level information. For example, idiosyncratic word usage and speaker-dependent pronunciation are high-level features for recognizing speakers. These high-level features can be combined with conventional features for increased accuracy. The seminar presents new methods to increase robustness and improve calibration of speaker recognition systems by addressing common factors in the forensic domain that degrade recognition performance. We describe MIT Lincoln Laboratory's VOCALINC system and its application to automated voice comparison of speech samples for law enforcement investigation and forensic applications. The talk concludes with appropriate uses of this technology, especially cautions regarding forensic-style applications, and a look at this technology's future directions.