Deep learning for emotional speech recognition
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 a...
| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | English English |
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AIP Publishing
2020
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/82389/ http://irep.iium.edu.my/82389/8/Certificate%20ICEDSA%202020%20%20%2328%20Deep%20Learning%20for%20Emotional%20Speech%20Recognition.pdf http://irep.iium.edu.my/82389/18/82389%20Deep%20learning%20for%20emotional%20speech%20recognition.pdf |
| _version_ | 1848789290626056192 |
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| author | Alhamada, M. I. Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha |
| author_facet | Alhamada, M. I. Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha |
| author_sort | Alhamada, M. I. |
| building | IIUM Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T17:54:22Z |
| format | Proceeding Paper |
| id | iium-82389 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:54:22Z |
| publishDate | 2020 |
| publisher | AIP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-823892020-12-30T04:15:20Z http://irep.iium.edu.my/82389/ Deep learning for emotional speech recognition Alhamada, M. I. Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha T Technology (General) T10.5 Communication of technical information 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. AIP Publishing 2020-12-15 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/82389/8/Certificate%20ICEDSA%202020%20%20%2328%20Deep%20Learning%20for%20Emotional%20Speech%20Recognition.pdf application/pdf en http://irep.iium.edu.my/82389/18/82389%20Deep%20learning%20for%20emotional%20speech%20recognition.pdf 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. https://aip.scitation.org/doi/pdf/10.1063/5.0032381 10.1063/5.0032381 |
| spellingShingle | T Technology (General) T10.5 Communication of technical information Alhamada, M. I. Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha Deep learning for emotional speech recognition |
| title | Deep learning for emotional speech recognition |
| title_full | Deep learning for emotional speech recognition |
| title_fullStr | Deep learning for emotional speech recognition |
| title_full_unstemmed | Deep learning for emotional speech recognition |
| title_short | Deep learning for emotional speech recognition |
| title_sort | deep learning for emotional speech recognition |
| topic | T Technology (General) T10.5 Communication of technical information |
| url | http://irep.iium.edu.my/82389/ http://irep.iium.edu.my/82389/ http://irep.iium.edu.my/82389/ http://irep.iium.edu.my/82389/8/Certificate%20ICEDSA%202020%20%20%2328%20Deep%20Learning%20for%20Emotional%20Speech%20Recognition.pdf http://irep.iium.edu.my/82389/18/82389%20Deep%20learning%20for%20emotional%20speech%20recognition.pdf |