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Istituto di linguistica computazionale "Antonio Zampolli"

Torna all'elenco Contributi in atti di convegno anno 2019

Contributo in atti di convegno

Tipo: Contributo in atti di convegno

Titolo: Combining Electrodermal Activity and Speech Analysis towards a more Accurate Emotion Recognition System

Anno di pubblicazione: 2019

Formato: Elettronico

Autori: Greco A., Marzi C., Lanata A., Scilingo E.P., Vanello N.

Affiliazioni autori: Faculty of Engineering - University of Pisa, Institute for Computational Linguistics - CNR, Faculty of Engineering - University of Pisa, Faculty of Engineering - University of Pisa, Faculty of Engineering - University of Pisa

Autori CNR:

  • CLAUDIA MARZI

Lingua: inglese

Abstract: Current research in the emotion recognition field is exploring the possibility of merging the information from physiological signals, behavioural data, and speech. Electrodermal activity (EDA) is amongst the main psychophysiological arousal indicators. Nonetheless, it is quite difficult to be analyzed in ecological scenarios, like, for instance, when the subject is speaking. On the other hand, speech carries relevant information of subject emotional state and its potential in the field of affective computing is still to be fully exploited. In this work, we aim at exploring the possibility of merging the information from electrodermal activity (EDA) and speech to improve the recognition of human arousal level during the pronunciation of single affective words. Unlike the majority of studies in the literature, we focus on speakers' arousal rather than the emotion conveyed by the spoken word. Specifically, a support vector machine with recursive feature elimination strategy (SVM-RFE) is trained and tested on three datasets, i.e. using the two channels (i.e., speech and EDA) separately and then jointly. The results show that the merging of EDA and speech information significantly improves the marginal classifier (+11.64%). The six selected features by the RFE procedure will be used for the development of a future multivariate model of emotions.

Lingua abstract: inglese

Pagine da: 229

Pagine a: 232

Rivista:

Conference proceedings IEEE Service Center,
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1557-170X

Numero volume: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

ISBN: 978-1-5386-1311-5

DOI: 10.1109/EMBC.2019.8857745

Referee: Sė: Internazionale

Stato della pubblicazione: Published version

Indicizzato da:

  • INSPEC [19045145]
  • PubMed [https://www.ncbi.nlm.nih.gov/pubmed/31945884]
  • Scopus [2-s2.0-85077864872]

Parole chiave:

  • emotion recognition
  • feature selection
  • pattern classification
  • physiology
  • psychology
  • support vector machines
  • human arousal level
  • single affective words
  • EDA
  • electrodermal activity
  • speech analysis
  • emotion recognition system
  • speech processing

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8857745&isnumber=8856280

Congresso nome: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Congresso luogo: Berlin, Germany

Congresso data: 23-27 July 20

Congresso rilevanza: Internazionale

Congresso relazione: Contributo

Strutture CNR:

Allegati: IEEE_2019_Combining Electrodermal Activity and Speech Analysis towards a more Accurate Emotion Recognition System (application/pdf)
2019_IEEE_proceedings

 
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