Then, the resulting network is searched to select the best scoring word at each node.
table 4 reports the WER results obtained on tst2013 by ROVER methods fed with: different numbers of hypotheses (from 3 to 8), at different granularity levels (whole utterance vs. segment), ranked with different models (random, RR1, RR2 and MLR) trained with different sets of features 8 http://sourceforge.net/p/lemur/wiki/ RankLib/ (Basic, WordBased, Basic+WordBased).