Assessing an Application of Spontaneous Stressed Speech - Emotions Portal

  • Daniel Palacios-AlonsoEmail author
  • Carlos Lázaro-Carrascosa
  • Agustín López-Arribas
  • Guillermo Meléndez-Morales
  • Andrés Gómez-Rodellar
  • Andrés Loro-Álavez
  • Victor Nieto-Lluis
  • Victoria Rodellar-Biarge
  • Athanasios Tsanas
  • Pedro Gómez-Vilda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11486)


Detecting and identifying emotions expressed in speech signals is a very complex task that generally requires processing a large sample size to extract intricate details and match the diversity of human expression in speech. There is not an emotional dataset commonly accepted as a standard test bench to evaluate the performance of the supervised machine learning algorithms when presented with extracted speech characteristics. This work proposes a generic platform to capture and validate emotional speech. The aim of the platform is collaborative-crowdsourcing and it can be used for any language (currently, it is available in four languages such as Spanish, English, German and French). As an example, a module for elicitation of stress in speech through a set of online interviews and other module for labeling recorded speech have been developed. This study is envisaged as the beginning of an effort to establish a large, cost-free standard speech corpus to assess emotions across multiple languages.


Characterizing stress Data acquisition Stress behavior in human-computer interaction Cooperative framework Emotional stress 



This work is being funded by grants TEC2016-77791-C4-4-R (MINECO, Spain) and CENIE _ TECA – PARK_55_02 INTERREG V – A Spain – Portugal (POCTEP).


  1. 1.
    Ley Orgánica 3/2018, de 5 de diciembre, de Protección de Datos Personales y garantía de los derechos digitales - Agencia Estatal Boletín Oficial del Estado. Accessed 7 Jan 2019
  2. 2.
    Portal Codec - GFK Group. Accessed 7 Jan 2019
  3. 3.
    Arciuli, J., Villar, G., Mallard, D.: Lies, lies and more lies. In: Proceedings of the 31st Annual Conference of the Cognitive Science Society (CogSci 2009), pp. 2329–2334 (2009)Google Scholar
  4. 4.
    Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W.F., Weiss, B.: A database of German emotional speech. In: Interspeech, vol. 5, pp. 1517–1520 (2005)Google Scholar
  5. 5.
    Engberg, I.S., Hansen, A.V.: Documentation of the Danish emotional speech database DES. Internal AAU report, Center for Person Kommunikation, Denmark, p. 22 (1996)Google Scholar
  6. 6.
    Hansen, J.H., Bou-Ghazale, S.E., Sarikaya, R., Pellom, B.: Getting started with SUSAS: a speech under simulated and actual stress database. In: Eurospeech, vol. 97, pp. 1743–1746 (1997)Google Scholar
  7. 7.
    Her - Official Webpage (2013). Accessed 4 May 2015
  8. 8.
    Hofbauer, K., Petrik, S., Hering, H.: The ATCOSIM corpus of non-prompted clean air traffic control speech. In: LREC (2008)Google Scholar
  9. 9.
    Moore, E., Clements, M.A., Peifer, J.W., Weisser, L.: Critical analysis of the impact of glottal features in the classification of clinical depression in speech. IEEE Trans. Biomed. Eng. 55(1), 96–107 (2008)CrossRefGoogle Scholar
  10. 10.
    Muñoz-Mulas, C., et al.: KPCA vs. PCA study for an age classification of speakers. In: Travieso-González, C.M., Alonso-Hernández, J.B. (eds.) NOLISP 2011. LNCS (LNAI), vol. 7015, pp. 190–198. Springer, Heidelberg (2011). Scholar
  11. 11.
    Ramakrishnan, S.: Recognition of emotion from speech: a review. In: Speech Enhancement, Modeling and Recognition-Algorithms and Applications, p. 121 (2012)CrossRefGoogle Scholar
  12. 12.
    Robot and Frank - IMDB Webpage (2012). Accessed 4 May 2015
  13. 13.
    Rodellar, V., Palacios, D., Gomez, P., Bartolome, E.: A methodology for monitoring emotional stress in phonation. In: 2014 5th IEEE Conference on Cognitive Infocommunications (CogInfoCom), pp. 231–236. IEEE (2014)Google Scholar
  14. 14.
    Rodellar-Biarge, V., Palacios-Alonso, D., Nieto-Lluis, V., Gómez-Vilda, P.: Towards the search of detection in speech-relevant features for stress. Expert Syst. 32, 701–718 (2015)CrossRefGoogle Scholar
  15. 15.
    Ververidis, D., Kotropoulos, C.: A review of emotional speech databases. In: Proceedings of the Panhellenic Conference on Informatics (PCI), pp. 560–574 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Escuela Técnica Superior de Ingeniería Informática - Universidad Rey Juan CarlosMóstolesSpain
  2. 2.Neuromorphic Speech Processing Lab, Center for Biomedical TechnologyUniversidad Politécnica de MadridPozuelo de AlarcónSpain
  3. 3.Usher Institute of Population Health Sciences and InformaticsUniversity of EdinburghEdinburghUK
  4. 4.Hermosilla 60 Legal CounselorsMadridSpain

Personalised recommendations