A fused forensic text comparison system using lexical features, word and character n-grams

Presented as part of the Languages and Linguistics Seminar Series.

This study investigates the degree that the performance of a likelihood ratio (LR)-based forensic text comparison (FTC) system improves by using logistic-regression fusion on LRs that were separately estimated by three different procedures, involving lexical features, word-based N-grams and character-based N-grams. This study uses predatory chatlog messages. The number of words used for modelling each group of messages is 500 words. The performance of the FTC system is assessed in terms of its validity (= accuracy) and reliability (= precision) using the log-likelihood-ratio cost (Cllr) and 95% credible intervals (CI), respectively. It is demonstrated that 1) out of the three procedures, the lexical features procedure performed best in terms of Cllr; and that 2) the fused system outperformed all three of the single procedures. The Cllr value of the fused system is better than that of the procedure with lexical features by a value of 0.14. It is also reported that the validity and reliability of a system is negatively correlated; the fused system that yielded the best result in terms of Cllr has the worst CI value.

Canberra is a Mecca of forensic voice/text comparison studies. Four talks will be given in October to highlight recent developments and progress that have been made in this area. This is a public event.