Voice Recognition System to Support Learning Platforms Oriented to People with Visual Disabilities

  • Ruben GonzalezEmail author
  • Johnnathan Muñoz
  • Julián Salazar
  • Néstor Duque
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9739)


The use of a speech recognition system allows access to an simple and efficient interaction, among others, for people with disabilities. In this article, an automatic speech recognition system is presented. It was developed as a system that allows an easy adaptation to different platforms. The model is described with clarity and detail looking for reproducibility by researchers who wish to resume and advance in this field. The model is divided into four stages: acquisition of the data, preprocessing, feature extraction and pattern recognition. Information concerning the functionality of the system is presented in the section named experiments and results. Finally, conclusions are set and a future work is proposed, in order to improve the efficiency and quality of the system.


Voice command recognition Mel Frequency Cepstral Coefficients Assistive technology Universal access to education 



The research presented in this paper was partially funded by the COLCIENCIAS project entitled: “RAIM: Implementación de un framework apoyado en tecnologías móviles y de realidad aumentada para entornos educativos ubicuos, adaptativos, accesibles e interactivos para todos (Implementation of a framework supported by mobile technologies and augmented reality for ubiquitous, adaptive, accessible and interactive learning environments for all)” of the Universidad Nacional de Colombia, with code 1119-569-34172.


  1. 1.
    Aggarwal, R.K., Dave, M.: Recent trends in speech recognition systems. In: Tiwary, U., Siddiqui, T. (eds.) Speech, Image, and Language Processing for Human Computer Interaction: Multi-modal Advancements, pp. 101–127 (2012)Google Scholar
  2. 2.
    Rosdi, F., Ainon, R.N.: Isolated Malay speech recognition using Hidden Markov Models. In: International Conference on Computer and Communication Engineering, 2008. ICCCE 2008, pp.721–725, 13–15 May 2008Google Scholar
  3. 3.
    Bedoya, W.A., Munoz, L.D.: Methodology for voice commands recognition using stochastic classifiers. In: 2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA), pp. 66–71, 12–14 Sept. 2012Google Scholar
  4. 4.
    Ibarra, J.P., Guerrero, H.B.: Identificación de comandos de voz utilizando LPC y algoritmos genéticos en Matlab. Rev. CINTEX 15, 36–48 (2014)Google Scholar
  5. 5.
    Abushariah, A.A.M.; Gunawan, T.S.; Khalifa, O.O., Abushariah, M.A.M.: English digits speech recognition system based on Hidden Markov Models. In: 2010 International Conference on Computer and Communication Engineering (ICCCE), pp. 1–5, 11–12 May 2010Google Scholar
  6. 6.
    Gales, M., Young, S.: The application of hidden Markov models in speech recognition. Found. Trends Signal Process. 1(3), 195–304 (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    Ephraim, Y., Malah, D.: Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans. Acoust. Speech Signal Process. 32(6), 1109–1121 (1984)CrossRefGoogle Scholar
  8. 8.
    Ishizuka, K., Nakatani, T., Fujimoto, M., Miyazaki, N.: Noise robust voice activity detection based on periodic to aperiodic component ratio. Speech Commun. 52(1), 41–60 (2010)CrossRefGoogle Scholar
  9. 9.
    Zhang, X., Sun, J., Luo, Z.: One-against-all weighted dynamic time warping for language-independent and speaker-dependent speech recognition in adverse conditions. PLoS ONE 9(2), e85458 (2014). doi: 10.1371/journal.pone.0085458 CrossRefGoogle Scholar
  10. 10.
    Komatani, K., Hotta, N., SATO, S., Nakano, M.: Posteriori restoration of turn-taking and ASR results for incorrectly segmented utterances. IEICE Trans. Inf. Syst. E98.D(11), 1923–1931 (2015)CrossRefGoogle Scholar
  11. 11.
    Duque, N., Giraldo, M., Jaramillo, I.D., Salazar A.F.: GAIATools: Framework para la creación de objetos de aprendizaje accesibles. In: CAVA - VII Congreso Internacional de Ambientes Virtuales de Aprendizaje Adaptativos y Accesibles. Brasil (2015)Google Scholar
  12. 12.
    Hossan, M.A.; Memon, S.; Gregory, M.A.: A novel approach for MFCC feature extraction. In: 2010 4th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–5, 13–15 Dec. 2010Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ruben Gonzalez
    • 1
    Email author
  • Johnnathan Muñoz
    • 1
  • Julián Salazar
    • 1
  • Néstor Duque
    • 1
  1. 1.Universidad Nacional de ColombiaManizalesColombia

Personalised recommendations