The vast majority of speech understanding systems suffers from a bottleneck between the recognition and the interpretation components. Normally, only a relatively small set of word hypotheses is passed from the recognizer and no flow of information in the opposite direction is even possible. We propose an interaction scheme that tries to overcome many of the disadvantages of traditional systems. It makes use of the possibility to process abstract constituents in our word recognizer and pass them back as complex hypotheses. Predictions that define the complex analysis goal of the recognition can be derived dynamically during the interpretation of an utterance. A left-to-right processing in both recognition and interpretation makes an incremental analysis possible.