Po-Han Chi
Impact in
- Signal Processing top 2%
- Speech and Audio Processing
- Music and Audio Processing
- Artificial Intelligence top 2%
- Speech Recognition and Synthesis
- Natural Language Processing Techniques
- Topic Modeling
Papers in
-
- Music and Audio Processing 3
- Speech and Audio Processing 1
-
- Speech Recognition and Synthesis 4
- Topic Modeling 3
- Natural Language Processing Techniques 1
- Co-authors
- Hung-yi Lee (3 shared papers)Shu-Wen Yang (2 shared papers)Andy T. Liu (2 shared papers)Po‐Chun Hsu (1 shared paper)Shang-Wen Li (2 shared papers)Xuankai Chang (1 shared paper)Guan-Ting Lin (1 shared paper)Cheng-I Lai (1 shared paper)
- Journals
- Genome biology (1 paper)IEEE/ACM Transactions on Audio Speech and Language Processing (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- TaiwanUnited StatesUnited Kingdom
In The Last Decade
Po-Han Chi
5 papers receiving 1.2k citations
Po-Han Chi's Hit Papers
Peers
Comparison fields: 5 of 93
- Signal Processing 480
- Artificial Intelligence 725
- Experimental and Cognitive Psychology 111
- Molecular Biology 250
- Computer Vision and Pattern Recognition 73
Countries citing papers authored by Po-Han Chi
This map shows the geographic impact of Po-Han Chi'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 Po-Han Chi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Po-Han Chi more than expected).
Fields of papers citing papers by Po-Han Chi
This network shows the impact of papers produced by Po-Han Chi. 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 Po-Han Chi. The network helps show where Po-Han Chi may publish in the future.
Co-authors
The 20 scholars most cited alongside Po-Han Chi, 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 | SUPERB: Speech Processing Universal PERformance Benchmark Hit paper breakdown → | 2021 | 490 |
| 2 | 2019 | 307 | |
| 3 | 2020 | 238 | |
| 4 | 2021 | 130 | |
| 5 | 2024 | 26 |
About Po-Han Chi
Po-Han Chi is a scholar working on Signal Processing, Artificial Intelligence, Molecular Biology, Insect Science and Renewable Energy, Sustainability and the Environment, having authored 5 papers that have together received 1.2k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (4 papers), Topic Modeling (3 papers), Music and Audio Processing (3 papers), Bioinformatics and Genomic Networks (1 paper), Natural Language Processing Techniques (1 paper) and Speech and Audio Processing (1 paper). The work is most often cited by research in Signal Processing (480 citations), Artificial Intelligence (725 citations), Experimental and Cognitive Psychology (111 citations), Molecular Biology (250 citations) and Computer Vision and Pattern Recognition (73 citations). Po-Han Chi has collaborated with scholars based in Taiwan, United States and United Kingdom. Frequent co-authors include Hung-yi Lee, Shu-Wen Yang, Andy T. Liu, Po‐Chun Hsu, Shang-Wen Li, Xuankai Chang, Guan-Ting Lin, Cheng-I Lai, Yist Y. Lin and Yung-Sung Chuang. Their work appears in journals such as Genome biology, IEEE/ACM Transactions on Audio Speech and Language Processing and arXiv (Cornell University).
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.