Automatic transcript aligned and corrected This competition is arbitrated by a matching score Wi. Experimental context :-First experiments assessed on 3 hours of radio ESTER (with exact transcript and a 10% WER transcripts)-Second experiments assessed on 11 hours of RTBF on wich time stamps where manually added.-All words available in database are added to the language model-Language model : about 67000 words trained on « lemonde »-Speech recognition system : SPEERAL, an asynchronous decoder based on the A* algorithm. Results : Conclusions :-On ESTER tests approximative transcripts bring a WER gain of about 14% relative, while exact ones allows a WER gain close to 24% relative.-Spotting performance is good; more than 95.3% of segments have been found, with a precision of about 96.7%.-On RTBF tests, spotting performance is good; more than 95.3% of segments have been found, with a precision of about 96.7%. Fast-match to transcript island-The principle of the proposed method is close to approaches used in the field of information retrieval.-In our case, the hypothesis is a query which may be answered by one of the transcript island.-The lexicon is represented by a lexical space Ls where each dimension is associated to a word. The coefficients of these vectors represent the frequencies of words in the document.-As the current hypothesis is developed, a set of word clusters Ci is built and updated.-These clusters result from the intersection of hc and the transcript island Ii.-For each new word added to the hypothesis hc, transcript islands are considered as candidates for guiding the search.