| SNAP: Parallel processing applied to AI D Moldovan, W Lee, C Lin, M Chung Computer 25 (5), 39-49, 1992 | 45 | 1992 |
| Prosody-based measures for automatic severity assessment of dysarthric speech A Hernandez, S Kim, M Chung Applied Sciences 10 (19), 6999, 2020 | 38 | 2020 |
| Dysarthria Detection and Severity Assessment Using Rhythm-Based Metrics. A Hernandez, EJ Yeo, S Kim, M Chung INTERSPEECH, 2897-2901, 2020 | 38 | 2020 |
| Improving dysarthric speech intelligibility using cycle-consistent adversarial training SH Yang, M Chung arXiv preprint arXiv:2001.04260, 2020 | 32 | 2020 |
| Acoustic analysis of fricatives in dysarthric speakers with cerebral palsy A Hernandez, H Lee, M Chung Phonetics and Speech Sciences 11 (3), 23-29, 2019 | 28 | 2019 |
| Design and creation of dysarthric speech database for development of QoLT software technology DL Choi, BW Kim, YJ Lee, Y Um, M Chung 2011 International Conference on Speech Database and Assessments (Oriental …, 2011 | 24 | 2011 |
| Automatic generation of Korean pronunciation variants by multistage applications of phonological rules. JH Jeon, S Cha, M Chung, J Park, K Hwang ICSLP, 1998 | 24 | 1998 |
| Parallel natural language processing on a semantic network array processor M Chung, DI Moldovan IEEE Transactions on Knowledge and Data Engineering 7 (3), 391-405, 1995 | 23 | 1995 |
| Automatic pronunciation assessment of Korean spoken by L2 learners using best feature set selection H Ryu, H Hong, S Kim, M Chung 2016 Asia-Pacific Signal and Information Processing Association Annual …, 2016 | 22 | 2016 |
| Dysarthric Speech Database for Development of QoLT Software Technology. DL Choi, BW Kim, YW Kim, YJ Lee, Y Um, M Chung LREC, 3378-3381, 2012 | 22 | 2012 |
| Morpheme-based modeling of pronunciation variation for large vocabulary continuous speech recognition in Korean KN Lee, M Chung IEICE transactions on information and systems 90 (7), 1063-1072, 2007 | 21 | 2007 |
| Mispronunciation Diagnosis of L2 English at Articulatory Level Using Articulatory Goodness-Of-Pronunciation Features. H Ryu, M Chung SLaTE, 65-70, 2017 | 20 | 2017 |
| Applying parallel processing to natural-language processing M Chung, D Moldevan IEEE Expert 9 (1), 36-44, 1994 | 19 | 1994 |
| A corpus-based analysis of English segments produced by Korean learners H Hong, S Kim, M Chung Journal of Phonetics 46, 52-67, 2014 | 18 | 2014 |
| Self-imitating feedback generation using GAN for computer-assisted pronunciation training SH Yang, M Chung arXiv preprint arXiv:1904.09407, 2019 | 17 | 2019 |
| USC: Description of the SNAP system used for MUC-5 D Moldovan, S Cha, M Chung, T Gallippi, KJ Hendrickson, JT Kim, C Lin, ... Fifth Message Understanding Conference (MUC-5): Proceedings of a Conference …, 1993 | 17 | 1993 |
| One-pass semi-dynamic network decoding using a subnetwork caching model for large vocabulary continuous speech recongnition DH Ahn, M Chung IEICE TRANSACTIONS on Information and Systems 87 (5), 1164-1174, 2004 | 15 | 2004 |
| Automatic severity classification of dysarthric speech by using self-supervised model with multi-task learning EJ Yeo, K Choi, S Kim, M Chung ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 12 | 2023 |
| A corpus-based analysis of Korean segments produced by Japanese learners. H Hong, S Kim, M Chung SLaTE, 189-192, 2013 | 12 | 2013 |
| Effects of allophones on the performance of Korean speech recognition. H Hong, S Kim, M Chung INTERSPEECH 8, 241, 2008 | 12 | 2008 |
