Emotion recognition by machine is a challenging task, but it has great potential to make empathic human-machine communications possible. In conventional approaches that consist of feature extraction and classifier stages, extensive studies have devoted their effort to developing good feature representations, but relatively little effort was made to make proper use of the important temporal information in these features. In this paper, we propose a model combining features known to be useful for emotion recognition and deep neural networks to exploit temporal information when recognizing emotion status. A benchmark evaluation on EMO-DB demonstrates that the proposed model achieves a state-of-theart performance of 88.9% recognition rate.
John et al. exploited the eGeMAPS features of the temporal information and put them into EmNet which was designed as the classifier, the final result achieves a state-of-the-art performance of 88.9% recognition rate in EMO-DB database REF .
Emotion Recognition from Human Speech Using Temporal Information and Deep Learning
INTERSPEECH
Computer Science