Most current state-of-the-art automatic speaker recognition systems extract speaker-dependent features by looking at short-term spectral information. This approach ignores long-term information that can convey supra-segmental information, such as prosodics and speaking style. We propose two approaches that use the fundamental frequency and energy trajectories to capture long-term information. The first approach uses bigram models to model the dynamics of the fundamental frequency and energy trajectories for each speaker. The second approach uses the fundamental frequency trajectories of a pre-defined set of works as the speaker templates and then, using dynamic time warping, computes the distance between templates and the works from the test message. The results presented in this work are on Switchboard 1 using the NIS extended date evaluation design. We show that these approaches can achieve an equal error rate of 3.7% which is a 77% relative improvement over a system based on short-term pitch and energy features alone.