Abstract
Understanding emotions from voice signals is a crucial yet difficult aspect of human-computer interaction (HCI). Many methods, including many well-known speech analysis and classification methods, have been used to extract emotions from signals in the literature on speech emotion recognition (SER). Deep learning methodologies have recently been presented as a replacement to traditional SER procedures. This paper presents an overview of deep learning algorithms for speech-based emotion recognition and evaluates some recent work that makes use of these methods. The review discusses the databases used, the emotions retrieved, the advancements made in voice emotion recognition, and its limits.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
