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Assistive Tools for the Motor-Handicapped People Using Speech Technologies: Lithuanian Case

  • Vytautas Rudžionis
  • Rytis Maskeliūnas
  • Algimantas Rudžionis
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 97)

Abstract

The paper presents analysis of the possibilities to use voice technologies for the partial integration of people with disabilities. The particular interest has been expressed to the motor-handicapped people. The special wheelchair with the voice command recognition capabilities has been designed. Evaluation of command’s recognition accuracy shows high dependency on the proper detection of the utterance boundaries. The acoustic boundaries detection algorithm has been proposed. This algorithm allowed achieve high accuracy of the detection of acoustic events boundaries such as words or phrases even in the presense of high noise. The proper detection leads to the increased accuracy of voice commands recognition and the overall satisfaction of users.

Keywords

voice technology voice command recognition motor-handicapped people acoustic events detection of people speaking 

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References

  1. 1.
    Hawley, M., Green, P., Enderby, P., Cunnigham, S., Moore, R.: Speech technology for e-inclusion of people with physical disabilities and disordered speech. In: Proc. of Interspeech 2005, Lisbon, Portugal (2005)Google Scholar
  2. 2.
    Rogoff, B., Goodman Turkanis, C., Bartlett, L.: Learning together: Chikdren and adults in school community. Oxford University Press, New York (2001)Google Scholar
  3. 3.
    Macek, T., Kleindienst, J., Krchal, J., Seredi, L.: Multi-modal telephony services in hometal Intelligent Environments. In: 3rd IET International Conference, pp. 404–410 (2007)Google Scholar
  4. 4.
    Maskeliunas, R., Rudzionis, A., Rudzionis, V.: Advances on the Use of the Foreign Language Recognizer. In: Esposito, A., Campbell, N., Vogel, C., Hussain, A., Nijholt, A. (eds.) Second COST 2102. LNCS, vol. 5967, pp. 217–224. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Maskeliunas, R., Rudzionis, A., Ratkevicius, K., Rudzionis, V.: Investigation of Foreign Languages Models for Lithuanian Speech Recognition. Electronics and Electrical Engineering – Kaunas: Technologija 3(91), 37–42 (2009)Google Scholar
  6. 6.
    Rabiner, L.R., Sambur, M.R.: An Algorithm For Determining the Endpoints in Isolated Utterances. Bell System Tech. J. 54, 297–315 (1975)CrossRefGoogle Scholar
  7. 7.
    Ying, G.S., Mitchell, C.D., Jamieson, L.: Endpoint Detection of Isolated Utterances Based on a Modified Teager Energy Measurement. In: Proc. of ICASSP 1993, pp. 732–735 (1993)Google Scholar
  8. 8.
    Hoyt, J., Wechsler, H.: Detection of Human Speech in Structured Noise. In: Proc. of ICASSP 1994, pp. 237–240 (1994)Google Scholar
  9. 9.
    Scheirer, E., Slaney, M.: Construction of Robust Multifeature Speech / Music Discriminator. In: Proc. of ICASSP 1997, pp. 1331–1334 (1997)Google Scholar
  10. 10.
    Rudzionis, A., Rudzionis, V.: Noisy speech detection and endpointing. In: Proc. of ISCA Workshop “Voice Operated Telecom Services”, Ghent, Belgium, pp. 79–84 (May 2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vytautas Rudžionis
    • 1
    • 2
  • Rytis Maskeliūnas
    • 1
    • 2
  • Algimantas Rudžionis
    • 1
    • 2
  1. 1.Department of InformaticsVilnius university Kaunas facultyLithuania
  2. 2.Department of InformaticsKaunas university of technologyLithuania

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