In this paper we evaluate the growth of Automatic Speech Recognition systems in respect to the various forms of spectral analysis ways used. A straightforward analysis of platter and Gammatone filter banks used for spectral analysis compared with the direct use of FFT spectral values is taken into account. This analysis was supported understanding the effectiveness of existing Automatic Speech Recognition systems that are specifically targeted on platter and Gammatone filter banks compared with FFT spectral values. We discover that warping the FFT spectrum directly, instead of using filter bank averaging, provides an additional precise approximation to the sensory activity scales. Direct use of FFT spectral values are even as effective as using either Gammatone or Linear Prediction filter banks, as long as the feature extracted from the FFT spectral values takes into consideration a Gammatone or platter like frequency scale. Computing speech signals using FFT or filter bank spectral features and utilizing a method supported by a sliding block of spectral features, is shown to be simpler in terms of ASR accuracy.
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