DOI Publisher Details
{"key"=>"Campanile20231241", "type"=>"Conference paper", "bibtex"=>"@conference{Campanile20231241,\n author = {Campanile, Lelio and de Fazio, Roberta and Di Giovanni, Michele and Marrone, Stefano and Marulli, Fiammetta and Verde, Laura},\n title = {Inferring Emotional Models from Human-Machine Speech Interactions},\n year = {2023},\n journal = {Procedia Computer Science},\n volume = {225},\n pages = {1241 – 1250},\n doi = {10.1016/j.procs.2023.10.112}\n}\n", "author"=>"Campanile, Lelio and de Fazio, Roberta and Di Giovanni, Michele and Marrone, Stefano and Marulli, Fiammetta and Verde, Laura", "author_array"=>[{"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Roberta", "last"=>"Fazio", "prefix"=>"de", "suffix"=>""}, {"first"=>"Michele", "last"=>"Di Giovanni", "prefix"=>"", "suffix"=>""}, {"first"=>"Stefano", "last"=>"Marrone", "prefix"=>"", "suffix"=>""}, {"first"=>"Fiammetta", "last"=>"Marulli", "prefix"=>"", "suffix"=>""}, {"first"=>"Laura", "last"=>"Verde", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Lelio", "author_0_last"=>"Campanile", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Roberta", "author_1_last"=>"Fazio", "author_1_prefix"=>"de", "author_1_suffix"=>"", "author_2_first"=>"Michele", "author_2_last"=>"Di Giovanni", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Stefano", "author_3_last"=>"Marrone", "author_3_prefix"=>"", "author_3_suffix"=>"", "author_4_first"=>"Fiammetta", "author_4_last"=>"Marulli", "author_4_prefix"=>"", "author_4_suffix"=>"", "author_5_first"=>"Laura", "author_5_last"=>"Verde", "author_5_prefix"=>"", "author_5_suffix"=>"", "title"=>"Inferring Emotional Models from Human-Machine Speech Interactions", "year"=>"2023", "journal"=>"Procedia Computer Science", "volume"=>"225", "pages"=>"1241 – 1250", "doi"=>"10.1016/j.procs.2023.10.112", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183548328&doi=10.1016%2fj.procs.2023.10.112&partnerID=40&md5=40fa5982063fbd25e375c1ae365a69e4", "abstract"=>"Human-Machine Interfaces (HMIs) are getting more and more important in a hyper-connected society. Traditional HMIs are built considering cognitive features while emotional ones are often neglected, bringing sometimes such interfaces to misuse. As a part of a long run research, oriented to the definition of an HMI engineering approach, this paper concretely proposes a method to build an emotional-aware explicit model of the user starting from the behaviour of the human with a virtual agent. The paper also proposes an instance of this model inference process in voice assistants in an automatic depression context, which can constitute the core phase to realize a Human Digital Twin of a patient. The case study generated a model composed of Fluid Stochastic Petri Net sub-models, achieved after the data analysis by a Support Vector Machine. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)", "author_keywords"=>"Emotional State Inference; Human Digital Twin; Human Machine Interaction; Process Mining; Speech Analysis", "keywords"=>"Behavioral research; Emotion Recognition; Man machine systems; Petri nets; Stochastic models; Stochastic systems; Virtual reality; Emotional models; Emotional state; Emotional state inference; Human digital twin; Human machine interaction; Human Machine Interface; Human-machine; Longest run; Process mining; Speech interaction; Support vector machines", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 1; All Open Access, Gold Open Access"}