Speech Recognition Using Energy Parameters to Classify Syllables in the Spanish Language

  • Sergio Suárez Guerra
  • José Luis Oropeza Rodríguez
  • Edgardo M. Felipe Riveron
  • Jesús Figueroa Nazuno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


This paper presents an approach for the automatic speech re-cognition using syllabic units. Its segmentation is based on using the Short-Term Total Energy Function (STTEF) and the Energy Function of the High Frequency (ERO parameter) higher than 3,5 KHz of the speech signal. Training for the classification of the syllables is based on ten related Spanish language rules for syllable splitting. Recognition is based on a Continuous Density Hidden Markov Models and the bigram model language. The approach was tested using two voice corpus of natural speech, one constructed for researching in our laboratory (experimental) and the other one, the corpus Latino40 commonly used in speech researches. The use of ERO parameter increases speech recognition by 5% when compared with recognition using STTEF in discontinuous speech and improved more than 1.5% in continuous speech with three states. When the number of states is incremented to five, the recognition rate is improved proportionally to 97.5% for the discontinuous speech and to 80.5% for the continuous one.


Speech Recognition Speech Signal Inference Rule Automatic Speech Recognition Spanish Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Meneido, H., Neto, J.: Combination of Acoustic Models in Continuous Speech Recognition Hybrid Systems, INESC, Rua Alves Redol, 9, 1000- 029 Lisbon, Portugal (2000)Google Scholar
  2. 2.
    Meneido, H., Joâo, P., Neto, J., Luis, B., Almeida, L.: INESC-IST. Syllable Onset Detection Applied to the Portuguese Language. In: 6th European Conference on Speech Communication and Technology (EUROSPEECH 1999), Budapest, Hungary, September 5-9 (1999)Google Scholar
  3. 3.
    Suárez, S., Oropeza, J.L., Suso, K., del Villar, M.: Pruebas y validación de un sistema de reconocimiento del habla basado en sílabas con un vocabulario pequeño. In: Congreso Internacional de Computación CIC 2003, México, D.F (2003)Google Scholar
  4. 4.
    Wu, S.-L., Shire, M.L., Greenberg, S., Morgan, N.: Integrating Syllable Boundary Information into Speech Recognition. In: Proc. ICASSP (1998)Google Scholar
  5. 5.
    Rabiner, L., Juang, B.-H.: Fundamentals of Speech Recognition. Prentice Hall, Englewood CliffsGoogle Scholar
  6. 6.
    Serridge, B.: Análisis del Español Mexicano, para la construcción de un sistema de reconocimiento de dicho lenguaje. In: Grupo TLATOA, UDLA, Puebla, México (1993)Google Scholar
  7. 7.
    Fujimura, O.: UCI Working Papers in Linguistics. In: Proceedings of the South Western Optimality Theory Workshop (SWOT II), Syllable Structure Constraints, a C/D Model Perspective, vol. 2 (1996)Google Scholar
  8. 8.
    Wu, S.: Incorporating information from syllable-length time scales into automatic speech recognition. PhD Thesis, Berkeley University, California (1998)Google Scholar
  9. 9.
    Bilmes, J.A.: A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. International Computer Science Institute, Berkeley (1998)Google Scholar
  10. 10.
    Jesus, S.C.: A Hybrid System with Symbolic AI and Statistical Methods for Speech Recognition, Doctoral Thesis, University of Washington (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sergio Suárez Guerra
    • 1
  • José Luis Oropeza Rodríguez
    • 1
  • Edgardo M. Felipe Riveron
    • 1
  • Jesús Figueroa Nazuno
    • 1
  1. 1.Computing Research CenterNational Polytechnic InstituteMexico

Personalised recommendations