RED-LSTM: Real time emotion detection using LSTM
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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Brac University
2023
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| 在線閱讀: | http://hdl.handle.net/10361/19369 |
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10361-19369 |
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10361-193692023-08-09T21:02:02Z RED-LSTM: Real time emotion detection using LSTM Labiba, Mansura Rahman Jahura, Fatema Tuj Alam, Sadia Binte Morshed, Tasfia Rahman, Wasey Feroz, Farhan Mostakim, Moin Department of Computer Science and Engineering, Brac University Machine learning Speech emotion recognition Prediction RNN LSTM Real-time prediction Human-computer interaction. Artificial intelligence. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 27-29). The development of the Internet of Things and voice-based multimedia apps has allowed for the association and capture of several aspects of human behavior through the use of big data, which consists of trends and patterns. In the emotion of human speech, there is a latent representation of numerous aspects that are expressed. By mining audio-based data, it has been prioritized to extract sentiment from human speech. This capacity to recognize and categorize human emotion will be crucial for developing the next generation of AI. The machine will then begin to connect with human desires as a result. The audio-based data, such as voice emotion recognition, has not been able to produce results as accurate as those of text-based emotion recognition in terms of performance. For acoustic modal data, this study presents a combined strategy of feature extraction and data encoding with one hot vector embedding. When real-time data is available, LSTM has even employed an RNN based model to forecast the emotion that captures the human voice’s tone and signifies it. When predicting categorical emotion, the model has been assessed and shown to perform better than the other models by about 10%. The model has been tested against two benchmark datasets, RAVDESS and TESS, which contain voice actors’ renditions of eight different emotions. This model beat other cutting-edge models, achieving approximately 80% accuracy for weighted data and approximately 85% accuracy for unweighted data. Mansura Rahman Labiba Fatema Tuj Jahura Sadia Alam Tasfia Binte Morshed Wasey Rahman B. Computer Science and Engineering 2023-08-09T06:28:45Z 2023-08-09T06:28:45Z 2023 2023-01 Thesis ID: 20201227 ID: 18101181 ID: 18301200 ID: 18101173 ID: 18101178 http://hdl.handle.net/10361/19369 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 29 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Machine learning Speech emotion recognition Prediction RNN LSTM Real-time prediction Human-computer interaction. Artificial intelligence. |
| spellingShingle |
Machine learning Speech emotion recognition Prediction RNN LSTM Real-time prediction Human-computer interaction. Artificial intelligence. Labiba, Mansura Rahman Jahura, Fatema Tuj Alam, Sadia Binte Morshed, Tasfia Rahman, Wasey RED-LSTM: Real time emotion detection using LSTM |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Feroz, Farhan |
| author_facet |
Feroz, Farhan Labiba, Mansura Rahman Jahura, Fatema Tuj Alam, Sadia Binte Morshed, Tasfia Rahman, Wasey |
| format |
Thesis |
| author |
Labiba, Mansura Rahman Jahura, Fatema Tuj Alam, Sadia Binte Morshed, Tasfia Rahman, Wasey |
| author_sort |
Labiba, Mansura Rahman |
| title |
RED-LSTM: Real time emotion detection using LSTM |
| title_short |
RED-LSTM: Real time emotion detection using LSTM |
| title_full |
RED-LSTM: Real time emotion detection using LSTM |
| title_fullStr |
RED-LSTM: Real time emotion detection using LSTM |
| title_full_unstemmed |
RED-LSTM: Real time emotion detection using LSTM |
| title_sort |
red-lstm: real time emotion detection using lstm |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/19369 |
| work_keys_str_mv |
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| _version_ |
1814308155198275584 |