메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Wei Huang (Suzhou University) Haiyan Zhang (Suzhou University)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.6
발행연도
2023.12
수록면
457 - 465 (9page)
DOI
10.5573/IEIESPC.2023.12.6.457

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
With the rapid progress of information technology, its application to teaching has gradually become a hot topic in the education field. Augmented reality (AR) combines virtual and real characteristics that can improve comprehension in a virtual environment, bringing new development opportunities to oral English teaching. Based on integration of the Vuforia SDK in the U-nity3D augmented reality engine, this research applies AR technology to spoken English teaching, improves a convolutional neural network (CNN), and proposes an English speech recognition system based on a connectionist temporal classification (CTC)-CNN (maxout). The results from experiments varying the number of iterations and the loss value, the proposed model converges after 80 iterations with strong performance. In recognition of spoken English with or without noise, the accuracy of this method was highest at 0.957 and 0.894, respectively, which is better than the CTC-CNN (sigmoid) model. In recognizing six kinds of spoken English, the accuracy of the CTC-CNN (maxout) model stabilizes at about 95%, with the highest accuracy at 97%. The accuracy rate shows that the method can be effectively applied to oral English teaching, and can provide a new reference method for innovations in oral English teaching and the improvement of teaching efficiency.

목차

Abstract
1. Introduction
2. Related Work
3. Integrating U-nity3D and AR for Spoken English Teaching
4. Application Effect Analysis
5. Conclusion
References

참고문헌 (23)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0