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Speech Recognition Native Module Environment Inherent in Mobiles Devices

  • Blanca E. Carvajal-GámezEmail author
  • Erika Hernández Rubio
  • Amilcar Meneses Viveros
  • Francisco J. Hernández-Castañeda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9175)

Abstract

Applications on mobile devices have been characterized for their usability. The voice is a natural means of interaction between users and mobile devices. Traditional speech recognition algorithms work in controlled media are targeted to specific population groups (e.g. age, gender or language to name of few), and also require a lot of computational resources so that the algorithms are effective. Therefore, pattern recognition is performed in mobile applications as web services. However, this type of solution generates high dependence on Internet connectivity, so it is desirable to have an embedded module for this task that does not consume many computational resources and have a good level of effectiveness. This paper presents an embedded mobile systems for voice recognition module is presented. This module works in noisy environments, it works for any age of users and has proved that it can work for several languages.

References

  1. 1.
    Love, S.: Understanding Mobile Human-Computer Interaction. Elsevier, Amsterdam (2005)Google Scholar
  2. 2.
    Jacko, J.A.: Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, 3rd edn. CRC Press Inc., Boca Raton (2012)CrossRefGoogle Scholar
  3. 3.
    Bragdon, A., Nelson, E., Li, Y., Hinckley, K.: Experimental analysis of touch-screen gesture designs in mobile environments. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 403–412. ACM (2011)Google Scholar
  4. 4.
    Turk, M.: Multimodal interaction: a review. Pattern Recogn. Lett. 36, 189–195 (2014)CrossRefGoogle Scholar
  5. 5.
    Tzovaras, D.: Multimodal User Interfaces: From Signals to Interaction. Signals and Communication Technology. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Choi, J., You, K., Sung, W.: An fpga implementation of speech recognition with weighted finite state transducers. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1602–1605. IEEE (2010)Google Scholar
  7. 7.
    Cutajar, M., Gatt, E., Grech, I., Casha, O., Micallef, J.: Comparative study of automatic speech recognition techniques. IET Sig. Process. 7(1), 25–46 (2013)CrossRefGoogle Scholar
  8. 8.
    Fábián, T.: Confidence Measurement Techniques in Automatic Speech Recognition and Dialog Management. Der Andere Verlag, Tönning (2008)Google Scholar
  9. 9.
    Kumar, K., Liu, J., Lu, Y.H., Bhargava, B.: A survey of computation offloading for mobile systems. Mob. Netw. Appl. 18(1), 129–140 (2013)CrossRefGoogle Scholar
  10. 10.
    Kumar, K., Lu, Y.H.: Cloud computing for mobile users: can offloading computation save energy? Computer 43(4), 51–56 (2010)CrossRefGoogle Scholar
  11. 11.
    Hill, M.D., Marty, M.R.: Amdahl’s law in the multicore era. IEEE Comput. 41(7), 33–38 (2008)CrossRefGoogle Scholar
  12. 12.
    Isidro Ramírez, R., Meneses Viveros, A., Hernándes Rubio, E., Torres Hernández, I.M.: Differences of energetic consumption between java and jni android apps. In: International Symposium on Integrated Circuits (ISIC 2014). IEEE (2014)Google Scholar
  13. 13.
    Pearce, D.: Enabling new speech driven services for mobile devices: an overview of the etsi standards activities for distributed speech recognition front-ends. In: AVIOS 2000: The Speech Applications Conference, pp. 261–264 (2000)Google Scholar
  14. 14.
    Bahl, P., Han, R.Y., Li, L.E., Satyanarayanan, M.: Advancing the state of mobile cloud computing. In: Proceedings of the Third ACM Workshop on Mobile Cloud Computing and Services, pp. 21–28. ACM (2012)Google Scholar
  15. 15.
    Di Fabbrizio, G., Okken, T., Wilpon, J.G.: A speech mashup framework for multimodal mobile services. In: Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 71–78. ACM (2009)Google Scholar
  16. 16.
    Husnjak, S., Perakovic, D., Jovovic, I.: Possibilities of using speech recognition systems of smart terminal devices in traffic environment. Procedia Eng. 69, 778–787 (2014)CrossRefGoogle Scholar
  17. 17.
    Oviatt, S.: Multimodal interfaces. In: The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications, pp. 286–304 (2003)Google Scholar
  18. 18.
    Ons, B., Gemmeke, J.F., et al.: Fast vocabulary acquisition in an nmf-based self-learning vocal user interface. Comput. Speech Lang. 28(4), 997–1017 (2014)CrossRefGoogle Scholar
  19. 19.
    Carvajal-Gamez, B.E., Gallegos-Funes, F.J., Rosales-Silva, A.J.: Color local complexity estimation based steganographic (clces) method. Expert Syst. Appl. 40(4), 1132–1142 (2013)CrossRefGoogle Scholar
  20. 20.
    Mendel, J.M.: Fuzzy logic systems for engineering: a tutorial. Proc. IEEE 83(3), 345–377 (1995)CrossRefGoogle Scholar
  21. 21.
    GEORGE, J.K., Bo, Y.: Fuzzy sets and fuzzy logic, theory and applications (2008)Google Scholar
  22. 22.
    Carvajal-Gamez, B.E., Hernándes Rubio, E., Meneses Viveros, A., Hernandez-Castaneda, F.J.: Feature extraction for word recognition on a mobile device based on discrete wavelet transform. In: Advances in Computing Science, vol. 83. Instituo Politénico Nacional (2014)Google Scholar
  23. 23.
    Sears, A., Jacko, J.A.: The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications. CRC Press, USA (2007)CrossRefGoogle Scholar
  24. 24.
    Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 4, 580–585 (1985)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Blanca E. Carvajal-Gámez
    • 1
    Email author
  • Erika Hernández Rubio
    • 2
  • Amilcar Meneses Viveros
    • 3
  • Francisco J. Hernández-Castañeda
    • 2
  1. 1.Instituto Politécnico NacionalUnidad Profesional Interdisciplinaria y Tecnología AvanzadaMéxico D.F.Mexico
  2. 2.Instituto Politécnico Nacional, SEPI-ESCOMMéxico D.F.Mexico
  3. 3.Departamento de Computación, CINVESTAV-IPNMéxico D.F.Mexico

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