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IJMLC 2011 Vol.1(1): 1-6 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.1

Efficient Discrete Tchebichef on Spectrum Analysis of Speech Recognition

Ferda Ernawan and Nur Azman Abu

Abstract—Speech recognition is still a growing field of importance. The growth in computing power will open its strong potentials for full use in the near future. Spectrum analysis is an elementary operation in speech recognition. Fast Fourier Transform (FFT) has been a traditional technique to analyze frequency spectrum of the signals in speech recognition.FFT is computationally complex especially with imaginary numbers. The Discrete Tchebichef Transform (DTT) is proposed instead of the popular FFT. DTT has lower computational complexity and it does not require complex transform dealing with imaginary numbers. This paper proposes a novel approach based on 256 discrete orthonormal Tchebichef polynomials as efficient technique to analyze a vowel and a consonant in spectral frequency of speech recognition. The comparison between 1024 discrete orthonormal Tchebichef transform and 256 discrete orthonormal Tchebichef transform has been done. The preliminary experimental results show that 256 DTT has the potential to be more efficient to transform time domain into frequency domain for speech recognition. 256 DTT produces simpler output than 1024 DTT in frequency spectrum. At the same time, 256 Discrete Tchebichef Transform can produce concurrently four formants F1, F2, F3 and F4.

Index Terms—Speech recognition, spectrum analysis, Fast Fourier Transforms and Discrete Tchebichef Transform.

Ferda Ernawan is with the Faculty of Information and Communication Technology, Universitas Dian Nuswantoro (UDINUS), Semarang,Indonesia (e-mail: ferda1902@gmail.com).
Nur Azman Abu is with the Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka,Malaysia (e-mail: nura@utem.edu.my).


Cite: Ferda Ernawan and Nur Azman Abu, "Efficient Discrete Tchebichef on Spectrum Analysis of Speech Recognition,"International Journal of Machine Learning and Computing vol. 1, no. 1, pp. 1-6, 2011.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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