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베트남인 한국어 학습자와 한국인의 한국어 겹받침 발음 비교 연구
장경남,유광복,Jang, Kyungnam,You, Kwang-Bock 국제문화기술진흥원 2022 The Journal of the Convergence on Culture Technolo Vol.8 No.6
In this paper the comparative study on the pronunciation of Vietnamese learners and Koreans for the Korean syllable final double consonants was performed. For many errors and the suggested teaching methods related to the pronunciation of the Korean syllable final double consonants that were investigated and analyzed through linguistic research the results of this study by using the analysis tools of speech signal processing were confirmed. Thus, we suggest the new educational method in this paper. Using SVM, which is widely used in machine learning of artificial intelligence the pronunciation of Vietnamese learners and that of Koreans were compared. Being able to obtain the decision hyperplane of the SVM means that Vietnamese learners' pronunciation of the Korean syllable final double consonants is quite different from that of Koreans. Otherwise their pronunciation are pretty similar each other. The new teaching method presented in this paper is not only composed of writing and listening but is included things such as the speech signal waveform in the time domain and its corresponding energy that can be visualized to the learners.
A Study of Peak Finding Algorithms for the Autocorrelation Function of Speech Signal
소신애(Shin-Ae So),이강희(Kang-Hee Lee),유광복(Kwang-Bock You),임하영(Ha-Young Lim),박지수(Ji Su Park) 한국컴퓨터정보학회 2016 韓國컴퓨터情報學會論文誌 Vol.21 No.12
In this paper, the peak finding algorithms corresponding to the Autocorrelation Function (ACF), which are widely exploited for detecting the pitch of voiced signal, are proposed. According to various researchers, it is well known fact that the estimation of fundamental frequency (F0) in speech signal is not only very important task but quite difficult mission. The proposed algorithms, presented in this paper, are implemented by using many characteristics – such as monotonic increasing function - of ACF function. Thus, the proposed algorithms may be able to estimate both reliable and correct the fundamental frequency as long as the autocorrelation function of speech signal is accurate. Since the proposed algorithms may reduce the computational complexity it can be applied to the real-time processing. The speech data, is composed of Korean emotion expressed words, is used for evaluation of their performance. The pitches are measured to compare the performance of proposed algorithms.