Hirofumi Inaguma
- Artificial Intelligence top 1%
- Signal Processing top 1%
- Computer Vision and Pattern Recognition top 10%
- Experimental and Cognitive Psychology
- Information Systems
- Co-authors
- Shinji WatanabeTatsuya KawaharaTomoki HayashiWangyou ZhangTakaaki HoriNanxin ChenShigeki KaritaTakenori Yoshimura
- Topics
- Speech Recognition and Synthesis (28 papers)Natural Language Processing Techniques (26 papers)Topic Modeling (19 papers)
- Journals
- IEEE/ACM Transactions on Audio Speech and Language ProcessingarXiv (Cornell University)Interspeech 2022
- Partner nations
- JapanUnited StatesFrance
In The Last Decade
Hirofumi Inaguma
35 papers receiving 969 citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 913
- Signal Processing 519
- Computer Vision and Pattern Recognition 81
- Experimental and Cognitive Psychology 41
- Information Systems 21
Countries citing papers authored by Hirofumi Inaguma
This map shows the geographic impact of Hirofumi Inaguma'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 Hirofumi Inaguma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hirofumi Inaguma more than expected).
Fields of papers citing papers by Hirofumi Inaguma
This network shows the impact of papers produced by Hirofumi Inaguma. 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 Hirofumi Inaguma. The network helps show where Hirofumi Inaguma may publish in the future.
Co-authorship network of co-authors of Hirofumi Inaguma
This figure shows the co-authorship network connecting the top 25 collaborators of Hirofumi Inaguma. A scholar is included among the top collaborators of Hirofumi Inaguma 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 Hirofumi Inaguma. Hirofumi Inaguma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 11 | |
| 3 | 18 | |
| 4 | 1 | |
| 5 | 4 | |
| 6 | 12 | |
| 7 | 1 | |
| 8 | 6 | |
| 9 | 149 | |
| 10 | 13 | |
| 11 | 15 | |
| 12 | 26 | |
| 13 | 11 | |
| 14 | 3 | |
| 15 | 24 | |
| 16 | 26 | |
| 17 | 1 | |
| 18 | A Comparative Study on Transformer vs RNN in Speech Applicationsbreakdown → | 439 |
| 19 | 4 | |
| 20 | 12 |
About Hirofumi Inaguma
Hirofumi Inaguma is a scholar working on Artificial Intelligence, Signal Processing and Infectious Diseases, having authored 35 papers that have together received 1.0k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (28 papers), Natural Language Processing Techniques (26 papers) and Topic Modeling (19 papers). The work is most often cited by research in Signal Processing (519 citations), Artificial Intelligence (913 citations) and Computer Vision and Pattern Recognition (81 citations). Hirofumi Inaguma has collaborated with scholars based in Japan, United States and France. Frequent co-authors include Shinji Watanabe, Tatsuya Kawahara, Tomoki Hayashi, Wangyou Zhang, Takaaki Hori, Nanxin Chen, Shigeki Karita, Takenori Yoshimura, Xiaofei Wang and Masao Someki. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, arXiv (Cornell University) and Interspeech 2022.
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.