Ming-Hsiang Su
-
- Emotion and Mood Recognition 17
- Signal Processing top 5%
- Speech and Audio Processing 9
- Artificial Intelligence top 5%
- Topic Modeling 15
- Natural Language Processing Techniques 7
- Speech and dialogue systems 6
- Speech Recognition and Synthesis 6
- Sentiment Analysis and Opinion Mining 5
- Applied Psychology top 10%
-
- Advanced Data Compression Techniques 8
Ming-Hsiang Su
46 papers receiving 539 citations
Peers
Comparison fields: 5 of 76
- Experimental and Cognitive Psychology 238
- Signal Processing 115
- Artificial Intelligence 283
- Applied Psychology 44
- Computer Vision and Pattern Recognition 81
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-authorship network
The 21 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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 0 | |
| 6 | 2023 | 1 | |
| 7 | 2021 | 46 | |
| 8 | 2020 | 7 | |
| 9 | 2020 | 36 | |
| 10 | 2019 | 18 | |
| 11 | 2019 | 11 | |
| 12 | 2019 | 77 | |
| 13 | 2018 | 45 | |
| 14 | 2018 | 6 | |
| 15 | 2016 | 11 | |
| 16 | A Near-Reality Approach to Improve the e-Learning Open Courseware. | 2013 | 7 |
| 17 | 2012 | 1 | |
| 18 | 2011 | 2 | |
| 19 | 2010 | 2 | |
| 20 | 2005 | 2 |
About Ming-Hsiang Su
Ming-Hsiang Su is a scholar working on Experimental and Cognitive Psychology, Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition and Media Technology, having authored 48 papers that have together received 567 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (17 papers), Topic Modeling (15 papers), Speech and Audio Processing (9 papers), Advanced Data Compression Techniques (8 papers), Natural Language Processing Techniques (7 papers), Speech and dialogue systems (6 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 (238 citations), Signal Processing (115 citations), Artificial Intelligence (283 citations), Applied Psychology (44 citations) and Computer Vision and Pattern Recognition (81 citations). Ming-Hsiang Su has collaborated with scholars based in Taiwan, Slovakia and China. 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 Yi Chang. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, IEEE Transactions on Affective Computing, Electronics, The International Review of Research in Open and Distributed Learning and IEEE Access.
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.