Evaluation Metrics
1. Accuracy
Accuracy refers to the average accuracy of the model across all evaluation instances. The concept of correctness may vary from case to case, so we present the main accuracy measures considered in the measurement, the scenarios in which these measures are used, and the associated formal definitions.
1.1 Weighted average recall
WAR means the ratio of the number of correctly predicted samples to the total number of samples. WAR is used as the default Acc metric on datasets such as IEMOCAP, MELD, and MSP-podcast.
1.2 Unweighted average recall
UAR represents the ratio of the number of correctly predicted samples in each category to the total number of samples in that category, and then the ratio calculated for all categories is averaged. UAR is used as the default Acc metric on datasets such as IEMOCAP, MELD, and MSP-podcast.