Speech Emotion Recognition System Using Recurrent Neural Network in Deep Learning

Siddhant S. Patil, Shruti K. Patil, Ishwari S. Chankeshwara, Hrishikesh S. Rapatwar, Prof. Vidya V. Waykule
2022 International Journal for Research in Applied Science and Engineering Technology  
Abstract: In today's world, machine learning and deep learning together are enabling around 80% of the human interactions through the sheer ubiquity of the solutions provided by this domain. But one of the problems with the existing world is most of the people are not able to understand the actual emotional meaning and occurrence behind a person's speech. For instance, people having problem like Catatonia, etc. are not able to express themselves clearly or some industries which are considering
more » ... ome marketing strategy according to the customer mood, etc. can use this method. So, to bridge this gap between the people, it is important to develop a system that can assist them and then predict their emotional speech. This paper reviews the different approaches adopted to reduce the barrier of emotional communication which are already in existence and what methodology they used while doing so. In this context, we also present an approach of using the Recurrent Neural Network which is a part of Deep learning algorithms. The whole process of automated systems which continuously learn, adapt, and improve without much instruction is really fascinating. Our primary goal is to create a robust communication system through technologies that enable machines to respond correctly and reliably to human voices and provide useful and valuable services accordingly. In this review, an extensive report is made on the various approaches available for speech emotion recognition that has been done till now. All the model's and accuracy aspects are taken into consideration and are relayed according to it. Keywords: Deep Learning, Recurrent Neural Networks, Emotion Recognition, Speech Recognition, SER, RNN, Catatonia.
doi:10.22214/ijraset.2022.41112 fatcat:gbg7jfik6rff3k23inl6hvqsfa