IMPOSTURE CLASSIFICATION FOR TEXT-DEPENDENT SPEAKER VERIFICATION

Anthony Larcher (1) , Kong Aik Lee (1) , Bin Ma (1) , Haizhou Li (1)

1 Institute for Infocomm Research - I²R [Singapore]

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 impos-tures: 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 im-postures can help to prevent future impostures of the same type. In this work, we first propose a new verification score to reject Play-back 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 impos-tures. The performance of the speaker verification engine for impos-ture classification is significantly improved with the C llr decreasing by at least 29% compared to the original system.

Mots clés: Speaker verification; Text-Dependent; Impos-tures; Playback

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]

Anthony Larcher, Kong Aik Lee, Bin Ma, Haizhou Li. IMPOSTURE CLASSIFICATION FOR TEXT-DEPENDENT SPEAKER VERIFICATION. IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP), May 2014, Florence, Italy. ⟨hal-01927570⟩