This paper examines an approach of using lexical stress models to improve the speech recognition performance on spontaneous telephone speech. We analyzed the correlation of various pitch, energy, and duration measurements with lexical stress on a large corpus of spontaneous English utterances, and identified the most informative features of stress using classification experiments. We incorporated the stress models into the recognizer first-pass Viterbi search and obtained modest but statistically significant improvements over a state-of-the-art real-time performance on the Jupiter weather information domain.