An ICA-based method for stress classification from voice samples

  • Daniel Palacios
  • Victoria Rodellar
  • Carlos Lázaro
  • Andrés Gómez
  • Pedro GómezEmail author
Developing nature-inspired intelligence by neural systems


Emotion detection is a hot topic nowadays for its potential application to intelligent systems in different fields such as neuromarketing, dialogue systems, friendly robotics, vending platforms and amiable banking. Nevertheless, the lack of a benchmarking standard makes it difficult to compare results produced by different methodologies, which could help the research community improve existing approaches and design new ones. Besides, there is the added problem of accurate dataset production. Most of the emotional speech databases and associated documentation are either privative or not publicly available. Therefore, in this work, two stress-elicited databases containing speech from male and female speakers were recruited, and four classification methods are compared in order to detect and classify speech under stress. Results from each method are presented to show their quality performance, besides the final scores attained, in what is a novel approach to the field of study.


ICA PCA Speech Stress Classification 



This work has been funded by Grants TEC2016-77791-C4-4-R from the Ministry of Economic Affairs and Competitiveness of Spain and Teca-Park/MonParLoc FGCSIC CENIE-0348_CIE_6_E (InterReg Programme) V-A Spain – Portugal (POCTEP) (Grant No. CENIE_TECA-PARK_55_02).

Compliance with ethical standards

Conflict of interest

The authors declare not having any conflict of interest and that their research has been conducted in compliment with all ethical principles.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Biomedical TechnologyUniversidad Politécnica de MadridPozuelo de Alarcón, MadridSpain
  2. 2.Escuela Técnica Superior de Ingeniería InformáticaUniversidad Rey Juan CarlosMóstoles, MadridSpain

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