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

arXiv 2003.10724 ↗First posted 24 Mar 2020eess.AScited by 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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2020.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 3; 2025: 6.

13 papers.

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  1. 2021Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognitioncited by 12
  2. 2023Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformerscited by 11
  3. 2024The Whole Is Bigger Than the Sum of Its Parts: Modeling Individual Annotators to Capture Emotional Variabilitycited by 3
  4. 2021Less is More: Sparse Sampling for Dense Reaction Predictionscited by 2
  5. 2022SVLDL: Improved Speaker Age Estimation Using Selective Variance Label Distribution Learningcited by 2
  6. 2024Deep Learning for Assessment of Oral Reading Fluencycited by 1
  7. 2025Labels Generated by Large Language Models Help Measure People's Empathy in Vitrocited by 1
  8. 2025Universal Semantic Disentangled Privacy-preserving Speech Representation Learningcited by 1
  9. 2025Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Predictioncited by 1
  10. 2024Usefulness of Emotional Prosody in Neural Machine Translationcited by 0
  11. 2025United we stand, Divided we fall: Handling Weak Complementary Relationships for Audio-Visual Emotion Recognition in Valence-Arousal Spacecited by 0
  12. 2025MMMOS: Multi-domain Multi-axis Audio Quality Assessmentcited by 0
1 more
  1. 2025LAPS-Diff: A Diffusion-Based Framework for Singing Voice Synthesis With Language Aware Prosody-Style Guided Learningcited by 0