Imposture classification for text-dependent speaker verification

Anthony Larcher, Kong-Aik Lee, Bin Ma, Haizhou Li

Abstract: This work focuses on text-dependent speaker verification, where a user is required to chose and pronounce a customized pass-phrase to get authenticated. In this context, there are three types of impostures: an impostor pronouncing the correct pass-phrase, an impostor pronouncing a wrong pass-phrase and the most difficult one: an impostor playing back a recording of the target speaker pronouncing a wrong pass-phrase. Detecting and classifying different types of impostures can help to prevent future impostures of the same type. In this work, we first propose a new verification score to reject Playback impostures. This score allows a relative reduction of 90% of the equal error rate against Playback impostures while offering performance similar to the baseline text-dependent score against other types of impostures. As a second contribution, we show that the new score can be combined with an existing text-dependent verification score to improve the classification of the different types of impostures. The performance of the speaker verification engine for imposture classification is significantly improved with the C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">llr</sub> decreasing by at least 29% compared to the original system.