In machine learning, a deep belief network is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables, with connections between the layers but not between units within each layer.
Published in Chapter:
Voice and Speech Recognition Application in Emotion Detection: A Utility for Future Trends
Tushar Anand (ABES Engineering College, India),
Sarthak Panwar (ABES Engineering College, India), Shubham Kumar Sharma (ABES Engineering College, India),
Rohit Rastogi (ABES Engineering College, India), and Mayank Gupta (Eli Lilly and Company, India)
Copyright: © 2024
|Pages: 27
DOI: 10.4018/979-8-3693-1082-3.ch013
Abstract
Emotion detection from voice signals is needed for human-computer interaction (HCI), which is a difficult challenge. In the literature on speech emotion recognition, various well known speech analysis and classification methods have been used to extract emotions from signals. Deep learning strategies have recently been proposed as a workable alternative to conventional methods and discuss several recent studies have employed these methods to identify speech-based emotions. The review examines the databases used, the emotions collected, and the contributions to speech emotion recognition. The speech emotion recognition project was created by the research team, which recognizes human speech emotions. The research team developed this project using Python 3.6. The RAVDEESS dataset was also used since it contained eight distinct emotions expressed by all speakers. The RAVDESS dataset, Python programming languages, and Pycharm as an IDE were all used by the author team.