In this paper, we propose a speech recognition method based on the dual processing nature of speech perception, where vowel-like sounds and nonvowel-like sounds are processed differently. For this purpose, vowel-like portions and nonvowel-like portions of the speech signal are segmented first, and different distance measures are applied to each of them. In order to segment input utterance into vowel-like and nonvowel-like intervals, we also propose a segmentation algorithm based on a set of descriptive features derived from the auditory filter bank output. According to our recognition experiments, a reduction of over one third in recognition errors is possible with the proposed method in comparison with the conventional method. This method is particularly useful when a broad phonetic classifier is employed as a front-end stage for reducing the number of candidate words.