Training Along Labels

One iteratioon of the general training algorithm looks as follows:

	for each utterance 
	    1) load and preprocess the utterances features
	    2) get an alignment path from somewhere
	    3) whatever has to be trained, let it accumulate
	       the necessary training information

        whatever has to be trained, let it update its
        parameters according to the accumulated data

Here step 2) can be either the running of a Viterbi or a forward-backward alignment, or we can load an aready aligned path from a file, which we call labels-file.

Usually, training along labels is much faster than computing a forced alignment for every utterances.