Automatic Estimation of Intelligibility Measure for Consonants in Speech
Table 6: Hyper-parameters tuned separately for each stop consonant model.
['[BOLD] CV', '[BOLD] N [BOLD] CNN', '[BOLD] [w [BOLD] 1, w [BOLD] 2, w [BOLD] 3]', '[BOLD] N [BOLD] Batch', '[ITALIC] η', '[BOLD] P [BOLD] dropout']
The hyper-parameters for each model are trained by using the development data. These hyper-parameters include number of convolutional layers, time domain kernel size for each layer, learning rate for gradient descent optimization, batch size, and dropout rate. If the network has more than three convolutional layers, the time domain kernel size beyond the third layer is set equal to w3.