Onah, Daniel;
Ibrahim, Asia;
(2023)
Evaluating Speech Emotion Recognition through the lens of CNN & LSTM Deep Learning Models.
In:
Proceedings of the 11th IEEE International Conference on Big Data (IEEE BigData 2023).
Institute of Electrical and Electronics Engineers (IEEE)
(In press).
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Abstract
Speech Emotion Recognition (SER) is a fascinating area of research in machine learning. Researchers have been exploring different techniques to improve this field including using deep learning models, feature extraction methods and transfer strategies to improve the accuracy and robustness of SER models. The advancements in SER not only contribute to the field of artificial intelligence but also have the potential to enhance our understanding of human emotions and improve communication between humans and machines.
| Type: | Proceedings paper |
|---|---|
| Title: | Evaluating Speech Emotion Recognition through the lens of CNN & LSTM Deep Learning Models |
| Event: | 11th IEEE International Conference on Big Data (IEEE BigData 2023) |
| Location: | Sorrento, Italy |
| Dates: | 15th-18th December 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ieeexplore.ieee.org/Xplore/home.jsp |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Speech emotion recognition, CNN, LSTM, deep learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities > Dept of Information Studies |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10184283 |
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