Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network
Table 2: Performance of AGHMN models on IEMOCAP.
['Model', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] happy', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] happy', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] sad', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] sad', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] neutral', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] neutral', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] angry', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] angry', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] excited', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] excited', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] frustrated', '[BOLD] IEMOCAP: Emotion Classes  [ITALIC] frustrated', '[BOLD] IEMOCAP: Emotion Classes  [BOLD] Avg.', '[BOLD] IEMOCAP: Emotion Classes  [BOLD] Avg.', '[BOLD] IEMOCAP: Emotion Classes  [BOLD] Avg.']
For both datasets, we report the accuracy and F1-score We also report the macro-average of F1-score (mF1) to reflect the model performance on minority emotion classes, since the weighted-average is compromised by the majority classes. Each result provided by us in the tables is the average value of 10 times repeated experiments. BiF-AGRU attains the best overall performance with significant improvement over the strongest baseline DialogueRNN△ (+4.2% Acc, +3.7% F1, +5.1% mF1). For each emotion, our ADHMN models achieve at least competitive performance as DialogueRNN△. In particular, our models attain very large improvement on happy (at least +11.5% Acc, +17.3% F1), which is the emotion with the least utterances. This demonstrates the capability of our models in recognizing minority emotion classes.