In the context of spoken language interpretation, this paper introduces a stochastic approach to infer and compose semantic structures. Semantic frame structures are directly derived from word and basic concept sequences representing the users' utterances. A rule-based process provides a reference frame annotation of the speech training data. Then dynamic Bayesian networks are used to hypothesize frames from test data. The semantic frames used in this work are specialized on the task domain from the Berkeley FrameNet set. Experiments are reported on the French MEDIA dialog corpus. For all the data, the manual transcriptions and annotations at the word and concept levels are available. Tests are performed under 3 different conditions raising in difficulty wrt the errors in the word and concept sequence inputs. Three different stochastic models are compared and the results confirm the ability of the proposed probabilistic frameworks to carry out a reliable semantic frame annotation.