Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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    Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation
    (IEEE, 2021-12-06) Kooragama, K.G.C.M; Jayashanka, L. R. W. D; Munasinghe, J. A; Jayawardana, K. W; Tissera, M; Jayasingha, T. B
    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.
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    Voice-based Road Navigation System Using Natural Language Processing (NLP)
    (IEEE, 2018-12-21) Withanage, P; Liyanage, T; Deeyakaduwe, N; Dias, E; Thelijjagoda, S
    In a highly technological era, voice-based navigation systems play a major role in bridging the gap between man and machine. To overcome the difficulty in understanding the user's voice commands and natural language simulations, process the path with the user's turn by turn directions with the mention of key entities such as street names, landmarks, points of interest, connections and path mapping in an interactive interface, we propose a user-centric roadmap navigation mobile application called “Direct Me”. To generate the user's preferred path, the system will first convert audio streams to text through ASR using the Pocket Sphinx library, followed by Natural Language Processing (NLP) by taking advantage of Stanford CoreNLP Framework to retrieve navigation-related information and handle the path in the map using the Google Map API at the user's request. This system is used to provide an effective approach to translating natural language commands into a format that can be fully understood by machine and will benefit in the development of human-machine-oriented interface.