• Journal of Digital Forensics
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• v.13 no.1
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• pp.35-44
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• 2019

The voice-phishing is done by inducing victims to send money, only with voice through the personal information illegally obtained. The amount of damage caused by voice-phishing continues to increase every year, and it became a social problem. Recently, the Financial Supervisory Service (i.e. the FSS) in Republic of Korea has been collecting the voices of voice-phishing scamer from victims. In this paper, we describe an effective forensic speaker analysis method for detecting the voice from the same person compared with the large-scale speech files stored in database(DB), and apply the aforementioned forensic speaker analysis method with the collected voice-phising speech files from victims. At first, an i-vector of each speech file had been extracted from the DB, then, the cosine similarity matrix for the all speech files had been generated through the cosine distance among the extracted the i-vectors of all speech file in DB. In other words, it performed the speaker analysis as grouping a set of candidates with high common similarity among i-vectors of all speech files in DB. As a result of EER(Error Equal Rate) measurement for 6,724 speech files composed of 82 speakers, it was confirmed that the EER of the i-vector-based method is improved than that of the GMM-based method. Finally, as a result of comparing the collected 2,327 voice-phishing speech files collected by the FSS, it was shown that some of the speech files having similar voice features were grouped each other.