Keiichi Tokuda
- Artificial Intelligence top 0.5%
- Signal Processing top 0.2%
- Experimental and Cognitive Psychology top 5%
- Computer Vision and Pattern Recognition top 5%
- Physiology
- Co-authors
- Alan W. BlackTomoki TodaHeiga ZenMasahide NakamuraTakashi KitamuraTakashi MasukoKeiichiro OuraAlfred McClung Lee
- Topics
- Speech Recognition and Synthesis (9 papers)Speech and Audio Processing (8 papers)Speech and dialogue systems (5 papers)
- Journals
- IEEE Transactions on Audio Speech and Language ProcessingSpeech CommunicationIEICE Transactions on Information and Systems
- Partner nations
- JapanUnited StatesUnited Kingdom
In The Last Decade
Keiichi Tokuda
10 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 2.0k
- Signal Processing 1.7k
- Experimental and Cognitive Psychology 257
- Computer Vision and Pattern Recognition 194
- Physiology 102
Countries citing papers authored by Keiichi Tokuda
This map shows the geographic impact of Keiichi Tokuda'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 Keiichi Tokuda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keiichi Tokuda more than expected).
Fields of papers citing papers by Keiichi Tokuda
This network shows the impact of papers produced by Keiichi Tokuda. 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 Keiichi Tokuda. The network helps show where Keiichi Tokuda may publish in the future.
Co-authorship network of co-authors of Keiichi Tokuda
This figure shows the co-authorship network connecting the top 25 collaborators of Keiichi Tokuda. A scholar is included among the top collaborators of Keiichi Tokuda 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 Keiichi Tokuda. Keiichi Tokuda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | Statistical parametric speech synthesisbreakdown → | 793 |
| 4 | 18 | |
| 5 | 11 | |
| 6 | 7 | |
| 7 | 267 | |
| 8 | 173 | |
| 9 | Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectorybreakdown → | 683 |
| 10 | 168 |
About Keiichi Tokuda
Keiichi Tokuda is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 2.1k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (9 papers), Speech and Audio Processing (8 papers) and Speech and dialogue systems (5 papers). The work is most often cited by research in Signal Processing (1.7k citations), Artificial Intelligence (2.0k citations) and Experimental and Cognitive Psychology (257 citations). Keiichi Tokuda has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include Alan W. Black, Tomoki Toda, Heiga Zen, Masahide Nakamura, Takashi Kitamura, Takashi Masuko, Keiichiro Oura, Alfred McClung Lee, Kei Hashimoto and Yoshihiko Nankaku. Their work appears in journals such as IEEE Transactions on Audio Speech and Language Processing, Speech Communication and IEICE Transactions on Information 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.