Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2013.465.471ErnawanFerdaAzman AbuNurSuryanaNanna32013133Spectrum analysis has become an elementary operation in vowel
recognition. Fast Fourier Transform (FFT) has been used as a famous technique
to analyze frequency spectrum of the signal in vowel recognition. Traditionally,
vowel recognition required large FFT computation on each window. This study
has proposed the Discrete Tchebichef Transform (DTT) as a possible alternative
to the popular FFT. DTT has had lower computational complexity and it did not
require complex transform with imaginary numbers. This study has proposed an
approach based on 256 DTT for efficient vowel recognition. The method used a
simplify set of recurrence relation matrix to compute within each window. Unlike
the FFT, DTT has provided a simpler matrix setting which involves real coefficient
numbers only. The experiment on vowel recognition using 256 DTT, 1024 DTT and
1024 FFT has been conducted to recognize five vowels. The experimental results
have indicated the practical advantage of 256 DTT in terms of spectral frequency
and time taken for vowel recognition performance. 256 DTT has been produced
frequency formants that were relatively similar output of 1024 DTT and 1024
FFT in terms of vowel recognition. The 256 DTT has become potential to be a
competitive candidate for computationally efficient dynamic vowel recognition.]]>Ali, A., S. Bhatti and M.S. Mian,2006Abu, N.A., S.L. Wong, N.S. Herman and R. Mukundan,2010Bailey, D.H. and P.N. Swarztrauber,1994Ernawan, F. and N.A. Abu,2011Ganapathy, S., P. Motlicek and H. Hermansky,2010Khandoker, A.H., C.K. Karmakar and M. Palaniswami,2008Li, C. and S.V. Andersen,2006Mukundan, R.,2003Mukundan, R.,2004Peterson, G.E. and H.L. Barney,1952Schubert, W.K.,2005Vite-Frias, J.A., Rd.J. Romero-Troncoso and A. Ordaz-Moreno,2005