• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.95-101
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• 2012

In this paper, we introduce the automatic speaker identification system 'SPO(Supreme Prosecutors Office) Verifier'. SPO Verifier is a GMM(Gaussian mixture model)-UBM(universal background model) based automatic speaker recognition system and has been developed using Korean speakers' utterances. This system uses a channel compensation algorithm to compensate recording device characteristics. The system can give the users the ability to manage reference models with utterances from various environments to get more accurate recognition results. To evaluate the performance of SPO Verifier on Korean speakers, we compared this system with one of the most widely used commercial systems in the forensic field. The results showed that SPO Verifier shows lower EER(equal error rate) than that of the commercial system.

• MALSORI
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• no.43
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• pp.45-55
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• 2002

High reliability of automatic speaker recognition regardless of voice conditions is necessary for forensic application. Audio recordings in real cases are not consistent in voice conditions, such as duration, time interval of recording, given text or conversational speech, transmission channel, etc. In this study the variation of verification error rate of ASR system with the voice conditions was investigated. As a result in order to decrease both false rejection rate and false acception rate, the various voices should be used for training and the duration of train voices should be longer than the test voices.

• 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.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.77-80
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• 2002

This study proposes phonation type index k as a descriptor of the overall spectral tilt, which is free from the effects of fundamental frequency and vowel quality. The newly proposed phonation type index k presents a simple and single measure of the overall spectral tilt. Phonation type index k can be applied to speech technology. It can also be used in diagnosing patients voice qualities in speech pathology. The distribution of phonation type index k, which is speaker-dependent, may be useful in forensic phonetics and voice recognition as an indicator of speaker identity.