This paper describes a new speaker-independent speech recognition method, which effectively uses dynamic features of speech. The method uses a segment-based parameter which consists of a series of LPC cepstrum coefficients obtained during several frames, which involves the dynamic features. First, a segment-based matching based on a statistical distance measure is performed. To reduce the amount of calculation, the method utilizes a linear discriminant function for segment-level matching. Second, word-level matching is performed by accumulating segment-based likelihoods using either the DTff or the HMM. Experiments to recognize 100 Japanese city names uttered by 50 people show the validity of the present method.