Che-Wei Huang

563 citations
22 papers · 367 · h-index 10

Impact in

Papers in

    • Speech Recognition and Synthesis 6
    • Text and Document Classification Technologies 3
    • Neural Networks and Applications 2
    • Music and Audio Processing 6
    • Speech and Audio Processing 6

Che-Wei Huang

22 papers receiving 343 citations

Peers

Che-Wei Huang
Comparison fields: 5 of 74
  • Signal Processing 143
  • Experimental and Cognitive Psychology 125
  • Artificial Intelligence 178
  • Applied Psychology 21
  • Bioengineering 17
Replace Değer Ayata with:
Değer Ayata Türkiye
Nobuo Sato Japan
Dagmar Schuller Germany
Linlin Chao China
Kun-Yi Huang Taiwan
Dominik Schiller Germany
Emilia Parada‐Cabaleiro Germany
Tim Polzehl Germany
Radhakrishnan Srikanth United States
Che-Wei Huang relative to Değer Ayata Türkiye Değer Ayata's profile →
Citations per field
00.5×4.3×
Değer Ayata · 1×
Citations per year

Countries citing papers authored by Che-Wei Huang

Since Specialization
Citations

This map shows the geographic impact of Che-Wei Huang'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 Che-Wei Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Che-Wei Huang more than expected).

Fields of papers citing papers by Che-Wei Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Che-Wei Huang. 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 Che-Wei Huang. The network helps show where Che-Wei Huang may publish in the future.

Co-authors

The 25 scholars most cited alongside Che-Wei Huang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Che-Wei Huang Line = papers co-authored together Che-Wei Huang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201792
2 201664
3 201639
4 201534
5 202027
6 201325
7 201916
8 201515
9 20189
10 20109
11 20126
12 20214
13 20164
14 20164
15 20163
16 20163
17 20143
18 20223
19 20162
20 20182

About Che-Wei Huang

Che-Wei Huang is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Biomedical Engineering and Information Systems, having authored 22 papers that have together received 367 indexed citations. Recurring topics across this work include Music and Audio Processing (6 papers), Speech and Audio Processing (6 papers), Speech Recognition and Synthesis (6 papers), Emotion and Mood Recognition (4 papers), Spam and Phishing Detection (3 papers), Text and Document Classification Technologies (3 papers), Advanced Chemical Sensor Technologies (2 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Signal Processing (143 citations), Experimental and Cognitive Psychology (125 citations), Artificial Intelligence (178 citations), Applied Psychology (21 citations) and Bioengineering (17 citations). Che-Wei Huang has collaborated with scholars based in United States, Taiwan and Australia. Frequent co-authors include Shrikanth Narayanan, Kuan-Ta Chen, Vincent S. Tseng, Josh Jia-Ching Ying, Roland Maas, Chih‐Ting Lin, Zac E. Imel, Panayiotis Georgiou, David C. Atkins and Sri Harish Mallidi. Their work appears in journals such as ACM Transactions on Knowledge Discovery from Data, IEEE Sensors Journal, PeerJ Computer Science, Sensors and IEEE Transactions on Biomedical Circuits and 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.

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