• DocumentCode
    3584697
  • Title

    Fuzzy Logic vs. HMM for phoneme recognition

  • Author

    Ben Fredj, Ines ; Ouni, Kais

  • Author_Institution
    Higher Sch. of Technol. & Comput. Sci., Carthage Univ., Tunis, Tunisia
  • fYear
    2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    We report on a comparison of fuzzy and HMM phoneme recognizers of data Timit corpus. We aimed to check an optimal number of signal cepstral coefficients for both a1pproaches. For this purpose, we used different parameterization techniques such as MFCC, LPCC and PLP. Also, coefficient numbers has been varied from 12 to 39 including first and second derivatives and signal energy to introduce signal temporal variation. Results showed that an appropriate number of acoustic parameters lead to an extensive performance recognition for both systems.
  • Keywords
    fuzzy logic; hidden Markov models; speech recognition; HMM; LPCC parameterization techniques; MFCC parameterization techniques; PLP parameterization techniques; acoustic parameters; data Timit corpus; fuzzy logic; phoneme recognition; signal cepstral coefficients; signal energy; signal temporal variation; Analytical models; Markov processes; Mel frequency cepstral coefficient; Fuzzy Logic; HMM; LPCC; MFCC; PLP; Timit;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Sciences and Technologies in Maghreb (CISTEM), 2014 International Conference on
  • Type

    conf

  • DOI
    10.1109/CISTEM.2014.7076913
  • Filename
    7076913
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3584697