tech,21H921036,bq 
% reduction in error . We discuss
<term>

maximum a posteriori estimation

</term>
of
<term>
continuous density hidden

#19054
We discussmaximum a posteriori estimation of continuous density hidden Markov models (CDHMM). 
model,71H921036,bq 
maximum a posteriori estimation
</term>
of
<term>

continuous density hidden Markov models ( CDHMM )

</term>
. The classical
<term>
MLE reestimation

#19059
We discuss maximum a posteriori estimation ofcontinuous density hidden Markov models ( CDHMM ). 
tech,22H921036,bq 
models ( CDHMM )
</term>
. The classical
<term>

MLE reestimation algorithms

</term>
, namely the
<term>
forwardbackward

#19070
The classicalMLE reestimation algorithms, namely the forwardbackward algorithm and the segmental kmeans algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities. 
tech,82H921036,bq 
reestimation algorithms
</term>
, namely the
<term>

forwardbackward algorithm

</term>
and the
<term>
segmental kmeans algorithm

#19076
The classical MLE reestimation algorithms, namely theforwardbackward algorithm and the segmental kmeans algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities. 
tech,122H921036,bq 
forwardbackward algorithm
</term>
and the
<term>

segmental kmeans algorithm

</term>
, are expanded and
<term>
reestimation

#19080
The classical MLE reestimation algorithms, namely the forwardbackward algorithm and thesegmental kmeans algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities. 
other,192H921036,bq 
algorithm
</term>
, are expanded and
<term>

reestimation formulas

</term>
are given for
<term>
HMM with Gaussian

#19087
The classical MLE reestimation algorithms, namely the forwardbackward algorithm and the segmental kmeans algorithm, are expanded andreestimation formulas are given for HMM with Gaussian mixture observation densities. 
model,242H921036,bq 
reestimation formulas
</term>
are given for
<term>

HMM with Gaussian mixture observation densities

</term>
. Because of its adaptive nature

#19092
The classical MLE reestimation algorithms, namely the forwardbackward algorithm and the segmental kmeans algorithm, are expanded and reestimation formulas are given forHMM with Gaussian mixture observation densities. 
tech,63H921036,bq 
. Because of its adaptive nature ,
<term>

Bayesian learning

</term>
serves as a unified approach for

#19105
Because of its adaptive nature,Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling and corrective training. 
tech,173H921036,bq 
unified approach for the following four
<term>

speech recognition

</term>
applications , namely
<term>
parameter

#19116
Because of its adaptive nature, Bayesian learning serves as a unified approach for the following fourspeech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling and corrective training. 
tech,223H921036,bq 
recognition
</term>
applications , namely
<term>

parameter smoothing

</term>
,
<term>
speaker adaptation
</term>
,

#19121
Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namelyparameter smoothing, speaker adaptation, speaker group modeling and corrective training. 
tech,253H921036,bq 
namely
<term>
parameter smoothing
</term>
,
<term>

speaker adaptation

</term>
,
<term>
speaker group modeling
</term>

#19124
Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing,speaker adaptation, speaker group modeling and corrective training. 
tech,283H921036,bq 
</term>
,
<term>
speaker adaptation
</term>
,
<term>

speaker group modeling

</term>
and
<term>
corrective training
</term>

#19127
Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing, speaker adaptation,speaker group modeling and corrective training. 
tech,323H921036,bq 
<term>
speaker group modeling
</term>
and
<term>

corrective training

</term>
. New experimental results on all

#19131
Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling andcorrective training. 
tech,154H921036,bq 
provided to show the effectiveness of the
<term>

MAP estimation approach

</term>
. It is wellknown that there are

#19149
New experimental results on all four applications are provided to show the effectiveness of theMAP estimation approach. 