A Review on Speech Emotion Recognition Using Deep Learning and Attention Mechanism

Emotions are an integral part of human interactions and are significant factors in determining user satisfaction or customer opinion. speech emotion recognition (SER) modules also play an important role in the development of human-computer interaction (HCI) applications. A tremendous number of SER systems have been developed over the last decades. Attention-based deep neural networks (DNNs) have been shown as suitable tools for mining information that is unevenly time distributed in multimedia content. The attention mechanism has been recently incorporated in DNN architectures to emphasise also emotional salient information. This paper provides a review of the recent development in SER and also examines the impact of various attention mechanisms on SER performance. Overall comparison of the system accuracies is performed on a widely used IEMOCAP benchmark database.

Automated summary

Emotions are crucial in human interactions, and SER modules are important for HCI applications. Attention-based DNNs are effective in mining emotional information, and recent advancements have incorporated attention mechanisms to emphasise emotional salient information. This paper reviews the development of SER and evaluates the impact of various attention mechanisms on SER performance, with a comparison on the IEMOCAP benchmark database.