Evaluation of Error and Correlation-Based Loss Functions For Multitask Learning Dimensional Speech Emotion Recognition

Understand

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.