Information-theoretical comparison of likelihood ratio methods of forensic evidence evaluation

2007 ◽  
Author(s):  
Daniel Ramos ◽  
Joaquin Gonzalez-Rodriguez ◽  
Grzegorz Zadora ◽  
Janina Zieba-Palus ◽  
Colin Aitken
Keyword(s):  
Likelihood Ratio ◽  
Forensic Evidence ◽  
Theoretical Comparison ◽  
Evidence Evaluation

Probabilistic Evaluation of SMS Messages as Forensic Evidence

2013 ◽  
pp. 138-149
Author(s):  
Shunichi Ishihara
Keyword(s):  
Likelihood Ratio ◽  
Low Cost ◽  
Scientific Evidence ◽  
Short Message ◽  
Likelihood Ratios ◽  
Forensic Evidence ◽  
Probabilistic Evaluation ◽  
Popular Medium ◽  
Log Likelihood ◽  
Message Service

This study is one of the first likelihood ratio-based forensic text comparison studies in forensic authorship analysis. The likelihood-ratio-based evaluation of scientific evidence has started being adopted in many disciplines of forensic evidence comparison sciences, such as DNA, handwriting, fingerprints, footwear, voice recording, etc., and it is largely accepted that this is the way to ensure the maximum accountability and transparency of the process. Due to its convenience and low cost, short message service (SMS) has been a very popular medium of communication for quite some time. Unfortunately, however, SMS messages are sometimes used for reprehensible purposes, e.g., communication between drug dealers and buyers, or in illicit acts such as extortion, fraud, scams, hoaxes, and false reports of terrorist threats. In this study, the author performs a likelihood-ratio-based forensic text comparison of SMS messages focusing on lexical features. The likelihood ratios (LRs) are calculated in Aitken and Lucy’s (2004) multivariate kernel density procedure, and are calibrated. The validity of the system is assessed based on the magnitude of the LRs using the log-likelihood-ratio cost (Cllr). The strength of the derived LRs is graphically presented in Tippett plots. The results of the current study are compared with those of previous studies.

Accuracy assessment methods for likelihood ratio-based evidence evaluation

2010 ◽  
Vol 50 (1) ◽  
pp. 31
Author(s):  
D. Ramos ◽  
J. Gonzalez-Rodriguez ◽  
J.-J. Lucena-Molina
Keyword(s):  
Likelihood Ratio ◽  
Accuracy Assessment ◽  
Assessment Methods ◽  
Evidence Evaluation

Application of automatic speaker verification techniques for forensic evidence evaluation

2014 ◽  
Author(s):  
A. M. T. S. B. Adikari ◽  
S. Devadithya ◽  
A. R. S. T. Bandara ◽  
K. C. J. Dharmawardane ◽  
K. C. B. Wavegedara
Keyword(s):  
Speaker Verification ◽  
Forensic Evidence ◽  
Evidence Evaluation ◽  
Verification Techniques

Probabilistic Evaluation of SMS Messages as Forensic Evidence

2012 ◽  
Vol 4 (3) ◽  
pp. 47-57 ◽  
Author(s):  
Shunichi Ishihara
Keyword(s):  
Likelihood Ratio ◽  
Short Message Service ◽  
Low Cost ◽  
Scientific Evidence ◽  
Short Message ◽  
Likelihood Ratios ◽  
Forensic Evidence ◽  
Probabilistic Evaluation ◽  
Popular Medium ◽  
Message Service

This study is one of the first likelihood ratio-based forensic text comparison studies in forensic authorship analysis. The likelihood-ratio-based evaluation of scientific evidence has started being adopted in many disciplines of forensic evidence comparison sciences, such as DNA, handwriting, fingerprints, footwear, voice recording, etc., and it is largely accepted that this is the way to ensure the maximum accountability and transparency of the process. Due to its convenience and low cost, short message service (SMS) has been a very popular medium of communication for quite some time. Unfortunately, however, SMS messages are sometimes used for reprehensible purposes, e.g., communication between drug dealers and buyers, or in illicit acts such as extortion, fraud, scams, hoaxes, and false reports of terrorist threats. In this study, the author performs a likelihood-ratio-based forensic text comparison of SMS messages focusing on lexical features. The likelihood ratios (LRs) are calculated in Aitken and Lucy’s (2004) multivariate kernel density procedure, and are calibrated. The validity of the system is assessed based on the magnitude of the LRs using the log-likelihood-ratio cost (Cllr). The strength of the derived LRs is graphically presented in Tippett plots. The results of the current study are compared with those of previous studies.

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