Deep Net Features for Complex Emotion Recognition
This work addresses emotion recognition for applications like human-computer interaction, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled complex emotion recognition, such as curiosity, by using features from pretrained deep networks for acoustic, audio-event, and lexical data, achieving an F1 score of 0.85 compared to a baseline of 0.69 on the EmoReact dataset.
This paper investigates the influence of different acoustic features, audio-events based features and automatic speech translation based lexical features in complex emotion recognition such as curiosity. Pretrained networks, namely, AudioSet Net, VoxCeleb Net and Deep Speech Net trained extensively for different speech based applications are studied for this objective. Information from deep layers of these networks are considered as descriptors and encoded into feature vectors. Experimental results on the EmoReact dataset consisting of 8 complex emotions show the effectiveness, yielding highest F1 score of 0.85 as against the baseline of 0.69 in the literature.