H91-1053 |
about 10 % was observed using
|
parameter smoothing
|
with prior densities estimated
|
J95-3002 |
is derived . The effects of the
|
parameter smoothing
|
techniques on the robust learning
|
W08-0124 |
number of classes is small , no
|
parameter smoothing
|
is needed . For the cases where
|
J95-3002 |
are first estimated by various
|
parameter smoothing
|
methods ( Good 1953 ; Katz 1987
|
H91-1053 |
Bayesian learning applied to HMM
|
parameter smoothing
|
had an overall 10 % reduction
|
H92-1036 |
adaptation , speaker group modeling ,
|
parameter smoothing
|
and corrective training . Tested
|
P06-1023 |
of an annotated parse tree . 3
|
Parameter Smoothing
|
We extracted the grammar from
|
H91-1053 |
serves as a unified approach for
|
parameter smoothing
|
, speaker adaptation , speaker
|
W03-1201 |
This illustrates the advantage of
|
parameter smoothing
|
. Bayesian Marginal Probs : corgi
|
H91-1053 |
serve as a unified approach for
|
parameter smoothing
|
, speaker adaptation , and speaker
|
H91-1053 |
preliminary results applying to HMM
|
parameter smoothing
|
, speaker adaptation , and speaker
|
H91-1053 |
smoothing : Since the goal of
|
parameter smoothing
|
is to obtain robust HMM parameters
|
N09-1069 |
performed for 20 iterations .4 No
|
parameter smoothing
|
was used . All runs used a fixed
|
H91-1052 |
retraining ( adaptation ) , and
|
parameter smoothing
|
. Experimentally , this approach
|
H92-1036 |
two types of applica - tions :
|
parameter smoothing
|
and adaptation learning . For
|
J95-3002 |
and real tasks . The effects of
|
parameter smoothing
|
for null events with Turing 's
|
H92-1031 |
They show that it is useful for
|
parameter smoothing
|
as well as for speaker adaptation
|
H91-1053 |
speaker adaptation . Therefore
|
parameter smoothing
|
and model adaptation in which
|
H91-1053 |
model with the prior densities . *
|
Parameter smoothing
|
: Since the goal of parameter
|
P06-1055 |
enabling us to do more SM cycles .
|
Parameter smoothing
|
leads to even better accuracy
|