Desperately seeking impostors: data-mining for competitive impostor testing in a text-dependent speaker verification system

Precise determination of the operating point of a real-world verification application is of great importance. For a text-dependent password-based security system, this can be a challenging task, as lexically matched impostor test data may be nonexistent. We present a data mining approach for extracting suitable impostor data. The approach may be applied to either the target database (the application data itself) or the stock databases (data from other applications). The method entails: 1) determining Levenstein distances of impostor text utterances with respect to the claimant password; 2) selecting subsets of impostor data at various levels of lexical distance; 3) calculating the score threshold using such subsets; 4) extrapolating the score threshold (and hence the operating point) for lexically perfectly-matched data. Experiments on four databases in two languages are presented. This approach, as applied to the target database, provides an accurate and inexpensive solution to a formidable real-world problem.