论文标题
评估多任务学习维度语音情感识别的错误和基于相关的损失功能
Evaluation of Error and Correlation-Based Loss Functions For Multitask Learning Dimensional Speech Emotion Recognition
论文作者
论文摘要
损失功能的选择是机器学习的关键部分。本文评估了在回归任务任务尺寸语音情绪识别中常用的两个不同的损失函数，一种基于错误和基于相关的损失函数。我们发现，使用基于误差的损耗函数（MSE误差（MSE）损耗（MSE）损耗（MAE），使用基于相关的损耗函数与一致性相关系数（CCC）损失相比，在平均CCC分数方面具有更好的性能。结果与两个输入功能集和两个数据集一致。这两个损失函数的测试预测散点图也证实了通过CCC分数测量的结果。
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.
