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한국인을 위한 영어 말하기 시험의 컴퓨터 기반 유창성 평가
장병용(Jang, Byeong-Yong),권오욱(Kwon, Oh-Wook) 한국음성학회 2014 말소리와 음성과학 Vol.6 No.2
In this paper, we propose an automatic fluency evaluation algorithm for English speaking tests. In the proposed algorithm, acoustic features are extracted from an input spoken utterance and then fluency score is computed by using support vector regression (SVR). We estimate the parameters of feature modeling and SVR using the speech signals and the corresponding scores by human raters. From the correlation analysis results, it is shown that speech rate, articulation rate, and mean length of runs are best for fluency evaluation. Experimental results show that the correlation between the human score and the SVR score is 0.87 for 3 speaking tests, which suggests the possibility of the proposed algorithm as a secondary fluency evaluation tool.
ICA와 DNN을 이용한 방송 드라마 콘텐츠에서 음악구간 검출 성능
허운행(Heo, Woon-Haeng),장병용(Jang, Byeong-Yong),조현호(Jo, Hyeon-Ho),김정현(Kim, Jung-Hyun),권오욱(Kwon, Oh-Wook) 한국음성학회 2018 말소리와 음성과학 Vol.10 No.3
We propose to use independent component analysis (ICA) and deep neural network (DNN) to detect music sections in broadcast drama contents. Drama contents mainly comprise silence, noise, speech, music, and mixed (speech+music) sections. The silence section is detected by signal activity detection. To detect the music section, we train noise, speech, music, and mixed models with DNN. In computer experiments, we used the MUSAN corpus for training the acoustic model, and conducted an experiment using 3 hours’ worth of Korean drama contents. As the mixed section includes music signals, it was regarded as a music section. The segmentation error rate (SER) of music section detection was observed to be 19.0%. In addition, when stereo mixed signals were separated into music signals using ICA, the SER was reduced to 11.8%.