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An ICA-based method for stress classification from voice samples

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Abstract

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.

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Acknowledgements

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).

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Authors and Affiliations

  1. Center for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223, Pozuelo de Alarcón, Madrid, Spain

    Daniel Palacios, Victoria Rodellar, Carlos Lázaro, Andrés Gómez & Pedro Gómez

  2. Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Calle Tulipán, s/n, 28933, Móstoles, Madrid, Spain

    Daniel Palacios & Carlos Lázaro

Authors
  1. Daniel Palacios
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  2. Victoria Rodellar
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  4. Andrés Gómez
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  5. Pedro Gómez
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Correspondence to Pedro Gómez.

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Palacios, D., Rodellar, V., Lázaro, C. et al. An ICA-based method for stress classification from voice samples. Neural Comput & Applic 32, 17887–17897 (2020). https://doi.org/10.1007/s00521-019-04549-3

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  • DOI: https://doi.org/10.1007/s00521-019-04549-3

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