Evaluation of Error and Correlation-Based Loss Functions For Multitask Learning Dimensional Speech Emotion Recognition
The choice of a loss function is a critical part in 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 correlation-based loss function with a concordance correlation coefficient (CCC) loss resulted better performance than error-based loss function with a mean squared error (MSE) loss, 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.
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IEMOCAP
MSP-IMPROV
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