tech,8-2-H92-1036,bq |
The classical
<term>
MLE reestimation algorithms
</term>
, namely the
<term>
forward-backward algorithm
</term>
and the
<term>
segmental k-means 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 the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities. |
tech,6-3-H92-1036,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,32-3-H92-1036,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 and corrective training . |