Detectors for accents and phrase boundaries have been developed which derive prosodic features from the speech signal and its fundamental frequency to support other modules of a speech understanding system in an early analysis stage, or in cases where no word hypotheses are available. The detectors' underlying Gaussian distribution classifiers were trained with 50 minutes and tested with 30 minutes of spontaneous speech, yielding recognition rates of 74% for accents and 86% for phrase boundaries. Since this material was prosodically hand labelled, the question was: which labels for phrase boundaries and accentuation were only guided by syntactic or semantic knowledge, and which ones are really prosodically marked? Therefore a small test subset has been resynthesized in such a way that comprehensibility was lost, but the prosodic characteristics were kept. This subset has been relabelled by 11 listeners with nearly the same accuracy as the detectors