Skip to main content
SpringerLink
Log in

CPSO Applied in the Optimization of a Speech Recognition System

  • Conference paper
Intelligent Data Engineering and Automated Learning – IDEAL 2014 (IDEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8669))

  • 1535 Accesses

Abstract

This paper proposes an optimization of a fuzzy inference system for the automatic recognition of numerical commands of voice using Chaotic Particle Optimization (CPSO). In addition preprocessing the speech signal with mel-frequency cepstral coefficients, we use the discrete cosine transform (DCT) to generate a two-dimensional temporal matrix used as input to a system of fuzzy implication to generate the pattern of the words to be recognized.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Serapião, A.: Fundamentos de Otimização por Inteligência de Enxames: Uma visão geral. Revista Controle & Automação 20(03) (2009)

    Google Scholar 

  2. Coppin, B.: Inteligência Artificial. In: Tradução e Revisão Técnica de Jorge Duarte Pires Valério, LTC. Rio de Janeiro, Brasil (2010)

    Google Scholar 

  3. da Luz, E.: Estimação de Fonte de Poluição Atmosférica Usando Otimização por Enxame de Partículas. Instituto Nacional de Pesquisas Espaciais, Tese de Mestrado, Brasil, São José dos Campos (2008)

    Google Scholar 

  4. Chiachia, G., Penteado, B., Marana, A.: Fusão de Métodos de Reconhecimento Facial Através da Otimização por Enxame de Partículas. In: UNESP - Faculdade de Ciências. Bauru - São Paulo, Brasil

    Google Scholar 

  5. Silveira, T., de Oliveira, H., da Silva, L.: Controle de Inércia para Fuga de Mínimos Locais de Funções Não-Lineares na Otimização por Enxame de Partículas. Universidade Federal de Alfenas, Brasil, Minas Gerais

    Google Scholar 

  6. Silveira, T., Miranda, V., de Oliveira, H.: Controle de Inércia Não-Monotônico para Fuga de Mínimos Locais na Otimização por Enxame de Partículas. Universidade Federal de Alfenas, Brasil, Minas Gerais

    Google Scholar 

  7. Silveira, T., de Oliveira, H., da Silva, L., Salgado, R.: Controle de Inércia Não Monotônico na Otimização por Enxame de Partículas. In: Interdisciplinary Studies in Computer Science, pp. 69–82 (July/December 2009)

    Google Scholar 

  8. Picone, J.W.: Signal modeling techiniques in speech recognition. IEEE Transactions on Computer 79(4), 1214–1247 (1991)

    Google Scholar 

  9. Rabiner, L., Biing-Hwang, J.: Fundamentals of Speech Recognition. Prentice Hall, New Jersey (1993)

    Google Scholar 

  10. Fissore, L., Laface, P., Rivera, E.: Using word temporal structure in HMM Speech recongnition. In: ICASSP 1997, Munich-Germany, vol. 2, pp. 975–978 (April 1997)

    Google Scholar 

  11. Ahmed, N., Natarajan, T., Rao, K.: Discrete Cosine Trasnform. IEEE Transaction on Computers c-24 edn. (January 1974)

    Google Scholar 

  12. Zhou, J., Chen, P..: Generalized Discrete Cosine Transform. In: 2009-Pacific-Asia Conference on Circuits, Communications and System, Chengdu, China (May 2009)

    Google Scholar 

  13. Hua, Y., Liu, W.: Generalized Karhunen–Loeve Transform. IEEE Signal Processing Letters 5(6) (June 1998)

    Google Scholar 

  14. Effros, M., Feng, H., Zeger, K.: Suboptimality of the Karhunen Lové Transform for Transform Coding. IEEE Transactions on Information Theory 50(8) (August 2004)

    Google Scholar 

  15. Zeng, J., Liu, Z.-Q.: Type-2 Fuzzy Hidden Markov Models and Their Application to Speech Recognition. IEEE Transactions on Fuzzy Systems 14(3) (June 2006)

    Google Scholar 

  16. Filho, O.P.C., Martinhon, C.A., Cabral, L.A.F.: Uma nova abordagem híbrida do algoritmo de Otimização por Enxame de Partículas com Busca Local Iterada para o problema de Clusterização de Dados. In: X Congresso Brasileiro de Inteligência Computacional, Porto de Galinhas - Ipojuca, Pernambuco, Brasil (2013)

    Google Scholar 

  17. Wang, L.-X.: A course in Fuzzy Systems and Control. Prentice Hall (1994)

    Google Scholar 

  18. Babuska, R.: Fuzzy Modeling for Control. Kluwer Academic Publishers (1998)

    Google Scholar 

  19. Chen, G.: Discussion of Approximation Properties of Minimum Inference Fuzzy System. In: Proceedings of the 29th Chinese Control Conference, Beijing, China, July 29-31 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electroelectronics, Laboratory of Computational Intelligence Applied to Technology, Federal Institute of Education, Science and Technology, Av. Getlio Vargas, 04, Monte Castelo, CEP: 65030-005, São Luis, Maranhão, Brazil

    Amanda Abelardo, Washington Silva & Ginalber Serra

Authors
  1. Amanda Abelardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Washington Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ginalber Serra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado  & Héctor Quintián  & 

  2. University of the Basque Country, Pasco Manuel de Lardizábal 1, 20018, San Sebastián, Spain

    José A. Lozano

  3. The University of Manchester, Sackville Street, M13 9PL, Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Abelardo, A., Silva, W., Serra, G. (2014). CPSO Applied in the Optimization of a Speech Recognition System. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10840-7_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10839-1

  • Online ISBN: 978-3-319-10840-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation