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Please use this identifier to cite or link to this item: http://hdl.handle.net/10791/369

Authors: Pennell, Ripley
Supervisor(s): Dr. Maiga Chang, Athabasca University
Examining Committee: Dr. Ali Dewan, Athabasca University
Dr. Kuo-Chen Li, Chung-Yuan Christian University
Degree: Master of Science, Information Systems (MScIS)
Department: Faculty of Science and Technology
Keywords: Natural Language Processing
Language Identification (LID)
Automatic Speech Recognition (ASR)
Mozilla Common Voice
Issue Date: 19-Jan-2022
Abstract: This research allows two individuals to speak their language with an application detecting what languages are being spoken, allowing automatic translation. Existing relevant Systematic Literature Reviews (SLRs) articulated the need for this research. An SLR with quantitative and qualitative analysis identified the best algorithm to use, the i-vector algorithm. To integrate it onto a mobile platform it had to be completely recreated, referencing Kaldi. A voice database was created using Mozilla Common Voice and four (4) models were trained using TensorFlow, each showing unique improvements. The final model is deployed in an Android application using Chaqoupy for environment translation. Evaluation produced an accuracy of 81% and a 95.7 on the System Usability Scale. Evaluation data was transformed for normality and analyzed using a one-way analysis of variance and a two independent samples t-test. This research can be applied to all languages and has no dependency on accents.
Graduation Date: Jun-2022
URI: http://hdl.handle.net/10791/369
Appears in Collections:Theses & Dissertations

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