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This paper presents a statistical method for the segmentation of words into syllables which is based on a joint n-gram model. Our system assigns syllable boundaries to phonetically transcribed words. The syllabification task was formulated as a tagging task. The syllable tagger was trained on syllable-annotated phone sequences. In an evaluation using ten-fold cross-validation, the system correctly predicted the syllabification of German words with an accuracy by word of 99.85%, which clearly exceeds results previously reported in the literature. The best performance was observed for a context size of five preceding phones. A detailed qualitative error analysis suggests that a further reduction of the error rate by up to 90% is possible by eliminating inconsistencies in the training database.