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Deep learning for emotional speech recognition

Alhamada, M. I. and Khalifa, Othman Omran and Hassan Abdalla Hashim, Aisha (2020) Deep learning for emotional speech recognition. In: 7th International Conference on Electronic Devices, Systems and Applications (ICEDSA2020), 28th - 29th March 2020, Shah Alam, Malaysia.

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Emotion speech recognition is a developing field in machine learning. The main purpose of this field is to produce a convenient system that is able to effortlessly communicate and interact with humans. Speech signals are loaded with information which is divided into two main categories, linguistic and paralinguistic; emotions belong to the latter tree. Developing systems that can understand paralinguistic information is paramount for better human-machine interactions. The complete reliability of the current speech emotion recognition systems is far from being achieved. To wit, the objective of this project is to review different methods used in speech emotion recognition SER. Different extracted features like MFCC as well as feature classifications methods like HMM, GMM, LTSTM and ANN are also researched. This research will also investigate different speech emotion databases that are commonly used. Finally, this paper implements an architecture of CNN that is used for speech emotion recognition. The proposed CNN model achieved 93.96% accuracy rate in detecting 5 emotions.

Item Type: Conference or Workshop Item (Plenary Papers)
Additional Information: 4119/82389 Virtual conference
Uncontrolled Keywords: deep learning, emotional speech recognition
Subjects: T Technology > T Technology (General)
T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Prof. Dr Othman O. Khalifa
Date Deposited: 27 Aug 2020 14:54
Last Modified: 30 Dec 2020 12:15
URI: http://irep.iium.edu.my/id/eprint/82389

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