Evaluation of error- And correlation-based loss functions for multitask learning dimensional speech emotion recognition

B. T. Atmaja, M. Akagi

Research output: Contribution to journalConference articlepeer-review

22 Citations (Scopus)

Abstract

The choice of a loss function is a critical part in machine learning. This paper evaluates two different loss functions commonly used in regression-task dimensional speech emotion recognition - error-based and correlation-based loss functions. We found that using correlation-based loss function with concordance correlation coefficient (CCC) loss resulted in better performance than error-based loss functions with mean squared error (MSE) and mean absolute error (MAE). The evaluations were measured in averaged CCC among three emotional attributes. 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.

Original languageEnglish
Article number012004
JournalJournal of Physics: Conference Series
Volume1896
Issue number1
DOIs
Publication statusPublished - 10 May 2021
Event1st Biennial International Conference on Acoustics and Vibration, ANV 2020 - Virtual, Online, India
Duration: 23 Nov 202024 Nov 2020

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