DOI: 10.5176/2382-5642_FSCR14.13
Authors: L. Sbattella, R. Tedesco, and A. Trivilini
Abstract: 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.
Keywords: information extraction, NLP, HMM
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