Forensic Examinations: Computational Analysis and Information Extraction

Complex interaction analysis, and information extraction from text and speech are an active research field, based on linguistic theories and NLP techniques. In this paper we present a conceptual model that aims at generating a rich description of forensic examinations, exploiting –in a novel way– forensic, psychological, and linguistic theories. Such description is then translated into profiles and a report. Profiles describe examiners and the person under examination, while the report evaluates the whole examination. Our main goal is to provide a didactical tool for improving forensic examination techniques. The model is based on a multi-layered set of HMMs, which leverage and combine speech (from audio recordings) and textual (from related transcriptions) features, classifying the examination at several granularity levels. Then, a rule-based expert system generates profiles and an evaluation of the examination. We also created a new audio/textual corpus based on real examinations collected from Italian trials. DIKE is the prototype we are currently implementing and that we plan to use for model validation.