Abstract
Two popular methods of feature extraction which have been applied to automatic speech recognition are linear prediction and Fast Fourier Transform analysis. Recent work by the authors has indicated that zero-crossing analysis methods also have the potential to result in accurate speech recognition. In this paper two studies for determining the relative applicability of each of these three feature extraction methods for speech recognition are presented. One study is aimed at determining the relative discriminability of the methods for vowel recognition. The other study is aimed at determining the noise vulnerability of each method. Several Fast Fourier Transform and zero-crossing analysis algorithms perform well in the classification of vowels in a quiet environment. Exceptional classification results are obtained for several zero-crossing analysis algorithms applied to vowels in noise.
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