Recorded speech and written texts are increasingly presented as scientific evidence in legal cases. This is due to the fact that the accessibility and anonymity of mobile phones and the internet mean that they are often exploited for criminal acts, but at the same time they leave records which must then be analysed as forensic evidence. This has led to a rapid growth of forensic voice/text comparison as a field of forensic science. The theories and techniques, which are necessary to analyse linguistic evidence, are introduced and demonstrated, with a particular focus on voice and text as linguistic evidence, using examples taken from various languages. In this course, we overview the process of forensic voice/text comparison, including extraction of individualising information from speech/text samples; modelling of speakers/authors, experimental procedures; calculation of evidential strength and performance assessment. Students will be trained so that they can apply their acquired skills and knowledge to actual linguistic data.
Upon successful completion, students will have the knowledge and skills to:
After successfully completing this course, students should be able to 1. Enhance the linguistic knowledge of the target language and beyond; 2. Explain the admissibility of scientific evidence; 3. Explain why the likelihood ratio-based framework is not only logically but also legally a correct way of analysing and presenting forensic evidence; 4. Understand the concept of likelihood ratio and Bayesian theorem; 5. Understand the nature of speech and text data as forensic evidence; 6. Extract individualising features from speech and text samples, and model them using appropriate statistical procedures; 7. Carry out forensic voice/text comparison tests; 8. Understand the concept of the metrics used in forensic voice/text comparison and appropriately use those metrics in the experiments; and 9. Understand and discuss the current issues surrounding forensic voice/text comparisons.
Summary assignment, 5%
Bibliography/Annotation assignment, 15%
Take-home assignments, 40%
Final exam: 40%
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Two lectures and one tutorial per week
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