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An emotional modulation model as signature for the identification of children developmental disorders.
Mencattini, Arianna; Mosciano, Francesco; Comes, Maria Colomba; Di Gregorio, Tania; Raguso, Grazia; Daprati, Elena; Ringeval, Fabien; Schuller, Bjorn; Di Natale, Corrado; Martinelli, Eugenio.
Afiliación
  • Mencattini A; Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Roma, Italy.
  • Mosciano F; Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Roma, Italy.
  • Comes MC; Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Roma, Italy.
  • Di Gregorio T; Faculty of Science MM.FF.NN., University of Bari, Aldo Moro, University Campus Ernesto Quagliariello, Via Edoardo Orabona 4, 70126, Bari, Italy.
  • Raguso G; Faculty of Science MM.FF.NN., University of Bari, Aldo Moro, University Campus Ernesto Quagliariello, Via Edoardo Orabona 4, 70126, Bari, Italy.
  • Daprati E; Department of Systems Medicine, CBMS, University of Rome Tor Vergata, via Montpellier 1, 00133, Roma, Italy.
  • Ringeval F; Laboratoire d'Informatique de Grenoble, Université Grenoble Alpes, 38401, St Martin d'Hères, France.
  • Schuller B; GLAM - Group on Language, Audio & Music, Imperial College London, SW7 2AZ, London, UK.
  • Di Natale C; Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, 86159, Augsburg, Germany.
Sci Rep ; 8(1): 14487, 2018 09 27.
Article en En | MEDLINE | ID: mdl-30262838
In recent years, applications like Apple's Siri or Microsoft's Cortana have created the illusion that one can actually "chat" with a machine. However, a perfectly natural human-machine interaction is far from real as none of these tools can empathize. This issue has raised an increasing interest in speech emotion recognition systems, as the possibility to detect the emotional state of the speaker. This possibility seems relevant to a broad number of domains, ranging from man-machine interfaces to those of diagnostics. With this in mind, in the present work, we explored the possibility of applying a precision approach to the development of a statistical learning algorithm aimed at classifying samples of speech produced by children with developmental disorders(DD) and typically developing(TD) children. Under the assumption that acoustic features of vocal production could not be efficiently used as a direct marker of DD, we propose to apply the Emotional Modulation function(EMF) concept, rather than running analyses on acoustic features per se to identify the different classes. The novel paradigm was applied to the French Child Pathological & Emotional Speech Database obtaining a final accuracy of 0.79, with maximum performance reached in recognizing language impairment (0.92) and autism disorder (0.82).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trastorno Autístico / Discapacidades del Desarrollo / Bases de Datos Factuales / Emociones / Modelos Psicológicos Tipo de estudio: Diagnostic_studies Límite: Adolescent / Child / Child, preschool / Female / Humans / Male Idioma: En Revista: Sci Rep Año: 2018 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trastorno Autístico / Discapacidades del Desarrollo / Bases de Datos Factuales / Emociones / Modelos Psicológicos Tipo de estudio: Diagnostic_studies Límite: Adolescent / Child / Child, preschool / Female / Humans / Male Idioma: En Revista: Sci Rep Año: 2018 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido