Imposture classification for text-dependent speaker verification
Anthony Larcher, Kong Aik Lee, Bin Ma, Haizhou Li · 2014
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 Cllrdecreasing by at least 29% compared to the original system.