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DC Field | Value | Language |
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dc.contributor.author | Kooragama, K.G.C.M | - |
dc.contributor.author | Jayashanka, L. R. W. D | - |
dc.contributor.author | Munasinghe, J. A | - |
dc.contributor.author | Jayawardana, K. W | - |
dc.contributor.author | Tissera, M | - |
dc.contributor.author | Jayasingha, T. B | - |
dc.date.accessioned | 2022-06-21T06:19:55Z | - |
dc.date.available | 2022-06-21T06:19:55Z | - |
dc.date.issued | 2021-12-06 | - |
dc.identifier.citation | K. G. C. M. Kooragama, L. R. W. D. Jayashanka, J. A. Munasinghe, K. W. Jayawardana, M. Tissera and T. Buddhika, "Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation," 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2021, pp. 0484-0490, doi: 10.1109/IEMCON53756.2021.9623163. | en_US |
dc.identifier.issn | 2644-3163 | - |
dc.identifier.uri | http://rda.sliit.lk/handle/123456789/2650 | - |
dc.description.abstract | Every English speaker wishes to expertise his/her public speaking skills sharply. However, it is extremely difficult and requires a significant amount of practice and experience on an individual basis. This paper introduces a novel online tool “Speech Master” to practice and improve public English speech delivering skills in a professional manner. Using natural language processing, machine learning, and deep learning approaches, the proposed system analyzes the user's speech in terms of content, grammatical accuracy, grammatical richness, facial expressions, and flow. The accuracy was checked by comparing actual results taken from experts with the predicted results obtained from the tool. “Speech Master” achieves an average accuracy of more than 80% and produces a better overall result. This novel tool benefits English speakers all over the world by meeting the demand for a simple and easy-to-use solution for improving or practicing English speech delivery skills; enhancing oratory skills, boosting confidence, and delivering well-articulated speeches. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartofseries | 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON); | - |
dc.subject | Speech Master | en_US |
dc.subject | Natural Language | en_US |
dc.subject | Processing | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Automated Speech | en_US |
dc.subject | Evaluation | en_US |
dc.subject | Approach | en_US |
dc.title | Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/IEMCON53756.2021.9623163 | en_US |
Appears in Collections: | Department of Computer Science and Software Engineering-Scopes Research Papers - Dept of Computer Science and Software Engineering Research Papers - IEEE Research Papers - SLIIT Staff Publications |
Files in This Item:
File | Description | Size | Format | |
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Speech_Master_Natural_Language_Processing_and_Deep_Learning_Approach_for_Automated_Speech_Evaluation.pdf Until 2050-12-31 | 939.59 kB | Adobe PDF | View/Open Request a copy |
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