Efficient Discrete Tchebichef on SpectrumAnalysis of Speech Recognition
Ferda Ernawan, Nur Azman Abu · International Journal of Machine Learning and Computing · 2011
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 F 1 , F 2 , F 3 and F 4 .