• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
Telecommunications Technology Association (TTA), Information and Communication Glossary, , http://terms.tta.or.kr/dictionary/ dictionaryView.do?word_seq=097616-1DOI
2 
H. J. Yoo, M. W. Kim, S. K. Park, K. Y Kim, 2020, Comparative Analysis of Korean Continuous Speech Recognition Accuracy by Application Field of Cloud-Based Speech Recognition Open API, The Journal of Korean Institute of Communications and Information Sciences, Vol. 45, No. 10, pp. 1793-1803DOI
3 
S. Y. Min, K. H. Lee, D. S. Lee, D. Y Ryu, 2020, A Study on Quantitative Evaluation Method for STT Engine Accuracy based on Korean Characteristics, Journal of the Korea Academia-Industrial cooperation Society, Vol. 21, No. 7, pp. 699-707DOI
4 
S. J. Choi, J. B Kim, 2017, Comparison analysis of speech recognition open APIs’ accuracy, Asia-pacific journal of multimedia services convergent with art, humanities, and sociology, Vol. 7, No. 8, pp. 411-418DOI
5 
H. K. Roh, K. H Lee, 2017, A Basic Performance Evaluation of the Speech Recognition APP of Standard Language and Dialect using Google, Naver, and Daum KAKAO APIs, Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, Vol. 7, No. 12, pp. 819-829DOI
6 
H. J. Yoo, S. Seo, S. W. Im, G. Y Gim, 2021, The performance evaluation of continuous speech recognition based on Korean phonological rules of cloud-based speech recognition open API, International Journal of Networked and Distributed Computing, Vol. 9, No. 1, pp. 10-18DOI
7 
P. Beça, J. Abreu, R. Santos, A Rodrigues, 2018, Evaluating the performance of ASR systems for TV interactions in several domestic noise scenarios, In Iberoamerican Conference on Applications and Usability of Interactive TV Springer, Springer, Cham, pp. 162-175DOI
8 
I. Siegert, Y. Sinha, O. Jokisch, A Wendemuth, 2020, Recognition performance of selected speech recognition APIs – A longitudinal study, In International Conference on Speech and Computer. Springer, Cham, pp. 520-529DOI
9 
T. Zhang, Y. Shao, Y. Wu, Y. Geng, L Fan, 2020, An overview of speech endpoint detection algorithms, Applied Acoustics, 160, 107133DOI
10 
C. Y Lee, 2010, Comparison of Male/Female Speech Features and Improvement of Recognition Performance by Gender- Specific Speech Recognition, The Journal of the Korea Institute of Electronic Communication Sciences, Vol. v.5, No. 6, pp. 568-574DOI