Paul Bender (Committee Member), Anna Lyon (Committee Member), Yong Pei (Advisor), Mateen Rizki (Committee Member)
Master of Science (MS)
Recent advances in computer-assisted, language-speaking, learning/training technology have demonstrated its promising potential to improve the outcome of language learning in early education, special education, English as a Second Language (ESL), and foreign language. The growing number of readily available mobile app-based solutions help encourage interest in learning to speak a foreign language, but their effectiveness is limited due to their lack of objective assessment and performance feedback resembling expert judgment. For example, it has been recognized that, in early education, students learn best with one-on-one instructions. Unfortunately, teachers do not have the time, and it is challenging to extend the learning to the home without the assistance of an independent learning/training tool. In this thesis research, our objective is to develop an effective and practical solution that will help people to learn and practice a new language independently at low cost. We have explored the use of real-time speech recognition, language translation, text synthesis, artificial intelligence (AI), and language intelligibility assessment technologies to develop a learning/training system that provides automatic assessment and instantaneous feedback of language-speaking performance in order to achieve an independent-learning workflow. Furthermore, we have designed and implemented a successful prototype system that demonstrates the feasibility and effectiveness of such a computer-assisted independent learning/training solution. This prototype can be easily used on a computer, tablet, smartphone, and other portable devices, and provides a new learning experience that is augmented and enhanced by objective assessment and significant feedback in order to improve the language-speaking proficiency of its user. Additionally, it may be used for real-time translation to support conversation across different languages. Our experimental results demonstrate that the proposed system can sufficiently analyze the intelligibility of one's speaking, accurately identify mispronounced words, and define a feedback that localizes and highlights errors for continuous practice toward perfection.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
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