Mmm.
and see which one is the  the  the best. Uh , well , the next part of the document is , well , basically , a kind of summary of what  everything that has been done. So. We have seventy - nine M L Ps trained on one , two , three , four , uh , three , four , five , six , seven ten  on ten different databases.
Mm - hmm.
Uh , the number of frames is bad also , so we have one million and a half for some , three million for other , and six million for the last one. Uh , yeah !  As we mentioned , TIMIT is the only that 's hand - labeled , and perhaps this is what makes the difference. Um. Yeah , the other are just Viterbi - aligned. So these seventy - nine MLP differ on different things. First , um with respect to the on - line normalization , there are  that use bad on - line normalization , and other good on - line normalization. Um. With respect to the features , with respect to the use of delta or no , uh with respect to the hidden layer size and to the targets. Uh , but of course we don't have all the combination of these different parameters Um. s What 's this ? We only have two hundred eighty six different tests And no not two thousand.