H92-1035 |
will concentrate on modifying the
|
N-best training
|
algorithm to model context in
|
P13-2098 |
truth annotations . Effect of
|
n-best training
|
size on WER The size of the training
|
H92-1035 |
further and perform what we call
|
N-best training
|
, which is a form of discriminative
|
H92-1031 |
and presents a technique called
|
N-best training
|
which improves the performance
|
H92-1035 |
utterance transcription , because
|
N-best training
|
directly optimizes the performance
|
W08-0119 |
that the computation required for
|
N-best training
|
is significantly increased since
|
H92-1035 |
rate to 11.6 % . When we used the
|
N-best training
|
( which used the SNN produced
|
H92-1035 |
confirming our belief that the
|
N-best training
|
is more effective than the 1-best
|
H92-1035 |
give an overall segment score .
|
N-best Training
|
In our latest version of the
|