TY - JOUR
T1 - Speaker forensic identification using joint factor analysis and i-vector
AU - Rouf, R. J.
AU - Arifianto, D.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2021/5/10
Y1 - 2021/5/10
N2 - Speaker Forensic is a process to determine the identity between a person's voice (known speaker) and the investigated voice (suspect speaker). To improve accuracy in speaker forensic analysis is used a combination of 2 forensic methods, joint factor analysis and i-vector methods. The forensic approach by adding noise signal with SNR value as a representation of a tapping situation to measure speaker identification performance. Classification on verification using i-vector is done by comparing i-vector model tests and targets. Both models calculated vector similarities using cosine similarity score. Verification is done with compare the same speaker and verification between different speakers The testing process on the program performance is indicated by an equal rate error value. While the system's sensibility is indicated by the threshold value. The results showed that the EER value of the Graz dataset (1.87%) compared to the Indonesia dataset (10%).
AB - Speaker Forensic is a process to determine the identity between a person's voice (known speaker) and the investigated voice (suspect speaker). To improve accuracy in speaker forensic analysis is used a combination of 2 forensic methods, joint factor analysis and i-vector methods. The forensic approach by adding noise signal with SNR value as a representation of a tapping situation to measure speaker identification performance. Classification on verification using i-vector is done by comparing i-vector model tests and targets. Both models calculated vector similarities using cosine similarity score. Verification is done with compare the same speaker and verification between different speakers The testing process on the program performance is indicated by an equal rate error value. While the system's sensibility is indicated by the threshold value. The results showed that the EER value of the Graz dataset (1.87%) compared to the Indonesia dataset (10%).
UR - http://www.scopus.com/inward/record.url?scp=85106157952&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1896/1/012026
DO - 10.1088/1742-6596/1896/1/012026
M3 - Conference article
AN - SCOPUS:85106157952
SN - 1742-6588
VL - 1896
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012026
T2 - 1st Biennial International Conference on Acoustics and Vibration, ANV 2020
Y2 - 23 November 2020 through 24 November 2020
ER -