Ming-Hsiang Su
Impact in
-
- Emotion and Mood Recognition
- Signal Processing top 5%
- Speech and Audio Processing
- Music and Audio Processing
Papers in
-
- Topic Modeling 16
- Speech and dialogue systems 8
- Natural Language Processing Techniques 7
- Speech Recognition and Synthesis 6
- Sentiment Analysis and Opinion Mining 5
-
- Emotion and Mood Recognition 17
- Co-authors
- Chung‐Hsien Wu (33 shared papers)Kun-Yi Huang (20 shared papers)Yi‐Hsuan Chen (1 shared paper)Hsin‐Min Wang (4 shared papers)Yu‐Ting Kuo (1 shared paper)Yuting Zheng (2 shared papers)Pao-Ta Yu (9 shared papers)Liangyu Chen (1 shared paper)
In The Last Decade
Ming-Hsiang Su
51 papers receiving 613 citations
Peers
Comparison fields: 5 of 78
- Experimental and Cognitive Psychology 250
- Signal Processing 128
- Applied Psychology 44
- Artificial Intelligence 314
- Computer Vision and Pattern Recognition 96
Countries citing papers authored by Ming-Hsiang Su
This map shows the geographic impact of Ming-Hsiang Su's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ming-Hsiang Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming-Hsiang Su more than expected).
Fields of papers citing papers by Ming-Hsiang Su
This network shows the impact of papers produced by Ming-Hsiang Su. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ming-Hsiang Su. The network helps show where Ming-Hsiang Su may publish in the future.
Co-authors
The 23 scholars most cited alongside Ming-Hsiang Su, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 84 | |
| 2 | 2017 | 53 | |
| 3 | 2021 | 52 | |
| 4 | 2018 | 51 | |
| 5 | 2020 | 46 | |
| 6 | 2018 | 45 | |
| 7 | 2018 | 44 | |
| 8 | 2016 | 37 | |
| 9 | 2019 | 20 | |
| 10 | 2016 | 20 | |
| 11 | 2018 | 14 | |
| 12 | 2016 | 12 | |
| 13 | 2019 | 12 | |
| 14 | 2016 | 11 | |
| 15 | 2020 | 10 | |
| 16 | 2017 | 10 | |
| 17 | 2018 | 10 | |
| 18 | 2019 | 10 | |
| 19 | A Near-Reality Approach to Improve the e-Learning Open Courseware. | 2013 | 9 |
| 20 | 2017 | 8 |
About Ming-Hsiang Su
Ming-Hsiang Su is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Computer Vision and Pattern Recognition, Signal Processing and Information Systems, having authored 53 papers that have together received 645 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (17 papers), Topic Modeling (16 papers), Speech and Audio Processing (9 papers), Speech and dialogue systems (8 papers), Advanced Data Compression Techniques (8 papers), Natural Language Processing Techniques (7 papers), Speech Recognition and Synthesis (6 papers) and Sentiment Analysis and Opinion Mining (5 papers). The work is most often cited by research in Experimental and Cognitive Psychology (250 citations), Signal Processing (128 citations), Applied Psychology (44 citations), Artificial Intelligence (314 citations) and Computer Vision and Pattern Recognition (96 citations). Ming-Hsiang Su has collaborated with scholars based in Taiwan, Slovakia and Vietnam. Frequent co-authors include Chung‐Hsien Wu, Kun-Yi Huang, Yi‐Hsuan Chen, Hsin‐Min Wang, Yi‐Hsuan Chen, Yu‐Ting Kuo, Yuting Zheng, Pao-Ta Yu, Liangyu Chen and Wei‐Po Lee. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, IEEE Transactions on Affective Computing, Sensors, Pattern Recognition and IEEE Transactions on Neural Networks and Learning Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.