We propose novel methods for integrating prosody in syntax using generative models. By adopting a grammar whose constituents have latent annotations, the influence of prosody on syntax can be learned from data. In one method, prosody is utilized to seed the latent annotations of a grammar which is then refined using EM iterations. In an orthogonal approach, we integrate prosody into grammar more explicitly using a model that jointly observes words and associated prosody. We evaluate the two methods by parsing speech data from the Switchboard corpus. The results are compared against baseline results from a model that does not use prosody. The experiments show that prosody improves a grammar in terms of accuracy as well as the parsimonious use of parameters.