1.Specifically, you have to train a basic model in order to train each feature
transformation, then retrain the model with the transformation applied to
the input features. This has to be done for each feature transformation
2.This means that it's necessary to define the set of acoustic classes on
which they are trained.
I am not clear...kindly explain what they mean
**
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Specifically, you have to train a basic model in order to train each feature
transformation, then retrain the model with the transformation applied to the
input features. This has to be done for each feature transformation
First LDA transform is trained on stage 01, then MLLT transform is trained on
stage 02
This means that it's necessary to define the set of acoustic classes on
which they are trained.
Current implementation separates CI phones. You define them during database
setup.
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
this is from ref
http://cmusphinx.sourceforge.net/wiki/ldamllt**
1.Specifically, you have to train a basic model in order to train each feature
transformation, then retrain the model with the transformation applied to
the input features. This has to be done for each feature transformation
2.This means that it's necessary to define the set of acoustic classes on
which they are trained.
I am not clear...kindly explain what they mean
**
First LDA transform is trained on stage 01, then MLLT transform is trained on
stage 02
Current implementation separates CI phones. You define them during database
setup.