Recent Papers

Convolutional neural network (CNN)-based speech emotion recognition is a new area of study that aims to automatically identify emotions in speech signals. This is a moving undertaking because of the perplexing idea of discourse signals, which contain different acoustic highlights that convey feelings like pitch, volume, and tone. In recent years, deep learning approaches, particularly CNNs, have outperformed conventional machine learning methods in speech and emotion recognition. CNNs have been extensively utilized in image and speech processing tasks due to their capability of automatically learning complex features from raw speech signals. CNN-based speech emotion recognition has numerous potential applications, including in the entertainment industry and healthcare, where it can be used to improve usersemotional experiences and to monitor patientsemotional states. In general, CNN-based speech emotion recognition is a rapidly developing field that has the potential to have a significant impact on a variety of fields. Further research in this field will result in the creation of more accurate and robust models for identifying emotions in speech signals.

Copyright © 2023 Karthick M. This is an open access article distributed under the Creative Commons Attribution License.