Min Chu
- Artificial Intelligence top 2%
- Signal Processing top 2%
- Experimental and Cognitive Psychology top 10%
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition
- Topics
- Speech Recognition and Synthesis (32 papers)Speech and Audio Processing (16 papers)Natural Language Processing Techniques (13 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Min Chu
57 papers receiving 612 citations
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 555
- Signal Processing 316
- Experimental and Cognitive Psychology 148
- Electrical and Electronic Engineering 86
- Computer Vision and Pattern Recognition 40
Countries citing papers authored by Min Chu
This map shows the geographic impact of Min Chu'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 Min Chu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min Chu more than expected).
Fields of papers citing papers by Min Chu
This network shows the impact of papers produced by Min Chu. 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 Min Chu. The network helps show where Min Chu may publish in the future.
Co-authorship network of co-authors of Min Chu
This figure shows the co-authorship network connecting the top 25 collaborators of Min Chu. A scholar is included among the top collaborators of Min Chu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Min Chu. Min Chu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 2 | |
| 3 | 7 | |
| 4 | Data Association Based on Target Topology | 2 |
| 5 | 2 | |
| 6 | Measuring attribute dissimilarity with HMM KL-divergence for speech synthesis. | 13 |
| 7 | 2 | |
| 8 | 37 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 1 | |
| 12 | 1 | |
| 13 | The Uncertainty in Prosody of Natural Speech and Its Application in Speech Synthesis | 3 |
| 14 | 1 | |
| 15 | 20 | |
| 16 | 48 | |
| 17 | A concatenative Mandarin TTS system without prosody model and prosody modification. | 14 |
| 18 | 1 | |
| 19 | A text-to-speech system with high intelligibility and naturalness for Chinese | 7 |
| 20 | A Chinese text-to-speech system with high intelligibility and high naturalness | 4 |
About Min Chu
Min Chu is a scholar working on Signal Processing, Artificial Intelligence and Experimental and Cognitive Psychology, having authored 59 papers that have together received 705 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (32 papers), Speech and Audio Processing (16 papers) and Natural Language Processing Techniques (13 papers). The work is most often cited by research in Signal Processing (316 citations), Artificial Intelligence (555 citations) and Experimental and Cognitive Psychology (148 citations). Min Chu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Eric Chang, D.J. Allstot, Yao Qian, Frank K. Soong, Yining Chen, Yong Zhao, Chao Huang, Yong Zhao, Peng Hu and Jia Liu. Their work appears in journals such as Sensors, IEEE Sensors Journal and IEEE Transactions on Audio Speech and Language Processing.
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.