Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2650
Title: Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation
Authors: Kooragama, K.G.C.M
Jayashanka, L. R. W. D
Munasinghe, J. A
Jayawardana, K. W
Tissera, M
Jayasingha, T. B
Keywords: Speech Master
Natural Language
Processing
Deep Learning
Automated Speech
Evaluation
Approach
Issue Date: 6-Dec-2021
Publisher: IEEE
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.
Series/Report no.: 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON);
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.
URI: http://rda.sliit.lk/handle/123456789/2650
ISSN: 2644-3163
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

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