TY - GEN
T1 - Towards measuring uniqueness of human voice
AU - Tandogan, Sinan E.
AU - Senear, Husrev T.
AU - Tavli, Bulent
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - The use of voice as a biometrie modality for user authentication and identification has grown very rapidly. It is therefore very important that we understand limitations of such systems which will ultimately depend on the discriminative power of the voice biometric. In this paper, we have contributed towards measuring distinctiveness of voice biometric by both formulating a new measure and creating a new dataset to perform more reliable measurements. For this purpose, we evaluate the prominent approaches in the field and propose a new approach that better incorporates within-user variability and is analytically more tractable. Our newly created dataset includes voice samples extracted from close to two thousand TED Talks videos. Overall our measurements on this dataset revealed a biometric information content of about 60 bits in human voice. Further, tests performed by adding some generic voice effects on the samples show that the distinctiveness reduces by almost 20 bits, implying that when true variability is reflected in user samples resulting entropy may further reduce.
AB - The use of voice as a biometrie modality for user authentication and identification has grown very rapidly. It is therefore very important that we understand limitations of such systems which will ultimately depend on the discriminative power of the voice biometric. In this paper, we have contributed towards measuring distinctiveness of voice biometric by both formulating a new measure and creating a new dataset to perform more reliable measurements. For this purpose, we evaluate the prominent approaches in the field and propose a new approach that better incorporates within-user variability and is analytically more tractable. Our newly created dataset includes voice samples extracted from close to two thousand TED Talks videos. Overall our measurements on this dataset revealed a biometric information content of about 60 bits in human voice. Further, tests performed by adding some generic voice effects on the samples show that the distinctiveness reduces by almost 20 bits, implying that when true variability is reflected in user samples resulting entropy may further reduce.
UR - http://www.scopus.com/inward/record.url?scp=85049788007&partnerID=8YFLogxK
U2 - 10.1109/WIFS.2017.8267666
DO - 10.1109/WIFS.2017.8267666
M3 - Conference contribution
AN - SCOPUS:85049788007
T3 - 2017 IEEE Workshop on Information Forensics and Security, WIFS 2017
SP - 1
EP - 6
BT - 2017 IEEE Workshop on Information Forensics and Security, WIFS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Workshop on Information Forensics and Security, WIFS 2017
Y2 - 4 December 2017 through 7 December 2017
ER -