  The choice of a loss function is a critical part of machine learning. This
paper evaluated two different loss functions commonly used in regression-task
dimensional speech emotion recognition, an error-based and a correlation-based
loss functions. We found that using a correlation-based loss function with a
concordance correlation coefficient (CCC) loss resulted in better performance
than an error-based loss function with mean squared error (MSE) loss and mean
absolute error (MAE), in terms of the averaged CCC score. The results are
consistent with two input feature sets and two datasets. The scatter plots of
test prediction by those two loss functions also confirmed the results measured
by CCC scores.
