We compare in this paper diverse hierarchical and multi-class approaches for the speech/music segmentation task, based on Support Vector Machines, combined with a median filter post-processing. We show the advantage of the multi-class approaches over the hierarchical schemes evaluated. Quantitative results provide a F-mesure over 96% that largely exceeds the results gathered by the ESTER evaluation campaign. We also show the relevance of the SVM with very low feature vector dimension on this task.