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

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

#19059
We discuss maximum a posteriori estimation ofcontinuous density hidden Markov models ( CDHMM ). 
other,192H921036,bq 
The classical
<term>
MLE reestimation algorithms
</term>
, namely the
<term>
forwardbackward algorithm
</term>
and the
<term>
segmental kmeans algorithm
</term>
, are expanded and
<term>
reestimation formulas
</term>
are given for
<term>
HMM with Gaussian mixture observation densities
</term>
.

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

#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. 
tech,154H921036,bq 
New experimental results on all four applications are provided to show the effectiveness of the
<term>
MAP estimation approach
</term>
.

#19149
New experimental results on all four applications are provided to show the effectiveness of theMAP estimation approach. 
tech,173H921036,bq 
Because of its adaptive nature ,
<term>
Bayesian learning
</term>
serves as a unified approach for the following four
<term>
speech recognition
</term>
applications , namely
<term>
parameter smoothing
</term>
,
<term>
speaker adaptation
</term>
,
<term>
speaker group modeling
</term>
and
<term>
corrective training
</term>
.

#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,21H921036,bq 
We discuss
<term>
maximum a posteriori estimation
</term>
of
<term>
continuous density hidden Markov models ( CDHMM )
</term>
.

#19054
We discussmaximum a posteriori estimation of continuous density hidden Markov models (CDHMM). 
tech,22H921036,bq 
The classical
<term>
MLE reestimation algorithms
</term>
, namely the
<term>
forwardbackward algorithm
</term>
and the
<term>
segmental kmeans algorithm
</term>
, are expanded and
<term>
reestimation formulas
</term>
are given for
<term>
HMM with Gaussian mixture observation densities
</term>
.

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

#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,63H921036,bq 
Because of its adaptive nature ,
<term>
Bayesian learning
</term>
serves as a unified approach for the following four
<term>
speech recognition
</term>
applications , namely
<term>
parameter smoothing
</term>
,
<term>
speaker adaptation
</term>
,
<term>
speaker group modeling
</term>
and
<term>
corrective training
</term>
.

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

#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. 