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

arXiv: https://arxiv.org/abs/2003.10724 · HTML: https://mlsanity.com/paper/2003.10724/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 13 of 13)

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## Abstract (arXiv)

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.