Publications tagged with "Emotional state"

1. Inferring Emotional Models from Human-Machine Speech Interactions

   Campanile, L., de Fazio, R., Di Giovanni, M., Marrone, S., Marulli, F., & Verde, L. (2023). Inferring Emotional Models from Human-Machine Speech Interactions [Conference paper]. Procedia Computer Science, 225, 1241–1250. https://doi.org/10.1016/j.procs.2023.10.112

   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)

   DOI Publisher Details Copy BibTeX Download .bib

   {"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"}
       

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2. Speech-based Evaluation of Emotions-Depression Correlation

   Verde, L., Campanile, L., Marulli, F., & Marrone, S. (2022). Speech-based Evaluation of Emotions-Depression Correlation. Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022. https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927758

   Abstract

   Early detection of depression symptoms is fundamental to limit the onset of further associated behavioural disorders, such as psychomotor or social withdrawal. The combination of Artificial Intelligence and speech analysis revealed the existence of objectively measurable physical manifestations for early detection of depressive symptoms, constituting a valid support to evaluate these signals. To push forward the research state-of-art, this aim of this paper is to understand quantitative correlations between emotional states and depression by proposing a study across different datasets containing speech of both depressed/non-depressed people and emotional-related samples. The relationship between affective measures and depression can, in fact, a support to evaluate the presence of depression state. This work constitutes a preliminary step of a study whose final aim is to pursue AI-powered personalized medicine by building sophisticated Clinical Decision Support Systems for depression, as well as other psychological disorders. © 2022 IEEE.

   DOI Publisher Details Copy BibTeX Download .bib

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