Multimodal and Multi-view Models for Emotion Recognition
Table 1: Data distribution of the USC-IEMOCAP dataset. F and M mean female and male speakers followed by their session number.
['[BOLD] Utterances', '[BOLD] Anger', '[BOLD] Happiness', '[BOLD] Neutral', '[BOLD] Sadness']
This dataset provides conversations between female and male speakers throughout five sessions. Each session involves a different pair of speakers, which accounts for a total of 10 speakers. The conversations are split into small utterances that map to emotion categories. The original emotion categories are merged to mitigate the unbalanced classes into four categories: anger, happiness, neutral, and sadness. We split the dataset using the one-speaker-out experimental setting. That is, we take four sessions for training, and the remaining session is split by speakers into the validation and test sets. We report our unweighted accuracy scores running 10-fold cross-validation experiments and averaging scores across folds.