Exact(57)
The data were split randomly into train and test subsets.
The experiments are separated into train and test part.
We use GeNIe/SMILEa to train, and test the BN.
Furthermore, the acoustic mismatch between train and test conditions (train with sFarsdat train and test using gFarsdat test or vice versa) intensifies the increase of WER.
The extracted features were then used to train and test the support vector machine (SVM) classifier.
The train and test sets correspond to the original splitting of the TIMIT database.
These are used to train and test classification and regression tree (CART).
Case 1: Neutral speech recognition with same train and test data.
As the names imply, they were used to train and test the networks consecutively.
Similar(2)
Data were randomized and divided into 10 matching train-and-test sets.
The data were randomized into 10 matching train-and-test sets.
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