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A tandem connectionist model using combination of multi-scale spectro-temporal features for acoustic event detection., , , , and . ICASSP, page 4293-4296. IEEE, (2012)Experimental investigation on the efficacy of Affine-DTW in the quality of voice conversion., , , and . APSIPA, page 119-124. IEEE, (2019)A measure of phonetic similarity to quantify pronunciation variation by using ASR technology., , , , , and . ICPhS, University of Glasgow, (2015)Cooking State Recognition based on Acoustic Event Detection., , and . CEA@ICMR, page 41-44. ACM, (2019)Voice conversion based on deep neural networks for time-variant linear transformations., , and . APSIPA, page 1259-1262. IEEE, (2017)A Revisit to Feature Handling for High-quality Voice Conversion Based on Gaussian Mixture Model., , , and . APSIPA, page 816-822. IEEE, (2018)Comparing Participants' Brainwaves During Solo, Pair, and Mob Programming., , , and . XP, volume 419 of Lecture Notes in Business Information Processing, page 200-209. Springer, (2021)INmfCA Algorithm for Training of Nonparallel Voice Conversion Systems Based on Non-Negative Matrix Factorization., , and . IEICE Trans. Inf. Syst., 105-D (6): 1196-1210 (2022)Quantifying Discriminability between NMF Bases., , and . ICASSP, page 691-695. IEEE, (2022)Prosodic Reading Tutor of Japanese, Suzuki-kun: The first and only educational tool to teach the formal Japanese., , and . SSW, page 122. ISCA, (2016)