We compare different methods for obtaining accurate speech  
segmentations starting from the corresponding orthography. The  
complete segmentation process can be decomposed into two basic    
steps. First, a phonetic transcription is automatically produced  
with the help of large vocabulary continuous speech recognition  
(LVCSR).  
Then, the phonetic information and the speech signal serve  
as input to a speech segmentation tool. We compare two automatic  
approaches to segmentation, based on the Viterbi and the  
Forward-Backward algorithm respectively. Further, we develop      
different techniques to cope with biases between automatic and    
manual segmentations. Experiments were performed to evaluate the  
generation of phonetic transcriptions as well as the different  
speech segmentation methods.