Keigo Nakamura

615 total citations
23 papers, 463 citations indexed

About

Keigo Nakamura is a scholar working on Artificial Intelligence, Signal Processing and Physiology. According to data from OpenAlex, Keigo Nakamura has authored 23 papers receiving a total of 463 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 19 papers in Signal Processing and 9 papers in Physiology. Recurrent topics in Keigo Nakamura's work include Speech Recognition and Synthesis (22 papers), Speech and Audio Processing (19 papers) and Voice and Speech Disorders (9 papers). Keigo Nakamura is often cited by papers focused on Speech Recognition and Synthesis (22 papers), Speech and Audio Processing (19 papers) and Voice and Speech Disorders (9 papers). Keigo Nakamura collaborates with scholars based in Japan and Germany. Keigo Nakamura's co-authors include Tomoki Toda, Kiyohiro Shikano, Hiroshi Saruwatari, Michael Wand, Tanja Schultz, Matthias Janke, Yoshitaka Nakajima, Makoto Otani, Tatsuya Hirahara and Takayuki Nagai and has published in prestigious journals such as The Journal of the Acoustical Society of America, Speech Communication and IEEE/ACM Transactions on Audio Speech and Language Processing.

In The Last Decade

Keigo Nakamura

23 papers receiving 422 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Keigo Nakamura Japan 12 397 385 137 55 28 23 463
Wen-Chin Huang Japan 13 426 1.1× 355 0.9× 65 0.5× 51 0.9× 11 0.4× 38 508
Miloš Cerňak Switzerland 14 365 0.9× 282 0.7× 85 0.6× 125 2.3× 23 0.8× 61 487
Szu-Chen Stan Jou United States 10 209 0.5× 208 0.5× 27 0.2× 40 0.7× 71 2.5× 11 342
Shansong Liu China 12 331 0.8× 255 0.7× 182 1.3× 99 1.8× 15 0.5× 31 432
Tamás Gábor Csapó Hungary 11 249 0.6× 211 0.5× 41 0.3× 56 1.0× 30 1.1× 62 347
Akira Sasou Japan 10 166 0.4× 176 0.5× 25 0.2× 109 2.0× 30 1.1× 51 299
Hynek Bořil United States 14 421 1.1× 412 1.1× 16 0.1× 116 2.1× 45 1.6× 41 539
Sorin Dusan United States 10 236 0.6× 231 0.6× 17 0.1× 76 1.4× 25 0.9× 24 321
Hannah Muckenhirn Switzerland 8 405 1.0× 416 1.1× 19 0.1× 26 0.5× 18 0.6× 11 487
Mats Blomberg Sweden 11 436 1.1× 310 0.8× 17 0.1× 106 1.9× 15 0.5× 54 488

Countries citing papers authored by Keigo Nakamura

Since Specialization
Citations

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

Fields of papers citing papers by Keigo Nakamura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Keigo Nakamura. 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 Keigo Nakamura. The network helps show where Keigo Nakamura may publish in the future.

Co-authorship network of co-authors of Keigo Nakamura

This figure shows the co-authorship network connecting the top 25 collaborators of Keigo Nakamura. A scholar is included among the top collaborators of Keigo Nakamura 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 Keigo Nakamura. Keigo Nakamura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Toda, Tomoki, et al.. (2013). Alaryngeal Speech Enhancement Based on One-to-Many Eigenvoice Conversion. IEEE/ACM Transactions on Audio Speech and Language Processing. 22(1). 172–183. 42 indexed citations
2.
Janke, Matthias, Michael Wand, Keigo Nakamura, & Tanja Schultz. (2012). Further investigations on EMG-to-speech conversion. 365–368. 19 indexed citations
3.
Nakamura, Keigo, Matthias Janke, Michael Wand, & Tanja Schultz. (2011). Estimation of Fundamental Frequency from Surface Electromyographic Data. International Conference on Acoustics, Speech, and Signal Processing. 6 indexed citations
4.
Nakamura, Keigo, Matthias Janke, Michael Wand, & Tanja Schultz. (2011). Estimation of fundamental frequency from surface electromyographic data: EMG-to-F<inf>0</inf>. 18 indexed citations
5.
Nakamura, Keigo, Tomoki Toda, Hiroshi Saruwatari, & Kiyohiro Shikano. (2011). Speaking-aid systems using GMM-based voice conversion for electrolaryngeal speech. Speech Communication. 54(1). 134–146. 123 indexed citations
6.
Nakamura, Keigo, et al.. (2011). An evaluation of alaryngeal speech enhancement methods based on voice conversion techniques. NAIST Digital Library (Nara Institute of Science and Technology). 20 indexed citations
7.
Nakamura, Keigo, Tomoki Toda, Hiroshi Saruwatari, & Kiyohiro Shikano. (2010). The use of air-pressure sensor in electrolaryngeal speech enhancement based on statistical voice conversion. 1628–1631. 6 indexed citations
8.
Nakamura, Keigo, et al.. (2010). Speaking-Aid Systems Based on One-to-Many Eigenvoice Conversion for Total Laryngectomees. NAIST Digital Library (Nara Institute of Science and Technology). 2 indexed citations
9.
Nakamura, Keigo, et al.. (2010). Esophageal Speech Enhancement Based on Statistical Voice Conversion with Gaussian Mixture Models. IEICE Transactions on Information and Systems. E93-D(9). 2472–2482. 30 indexed citations
10.
Nakamura, Keigo, Tomoki Toda, Hiroshi Saruwatari, & Kiyohiro Shikano. (2010). Evaluation of Extremely Small Sound Source Signals Used in Speaking-Aid System with Statistical Voice Conversion. IEICE Transactions on Information and Systems. E93-D(7). 1909–1917. 4 indexed citations
11.
Nakamura, Keigo, et al.. (2009). Enhancement of Esophageal Speech Using Statistical Voice Conversion. Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University). 805–808. 6 indexed citations
12.
Hirahara, Tatsuya, Makoto Otani, Tomoki Toda, et al.. (2009). Silent-speech enhancement using body-conducted vocal-tract resonance signals. Speech Communication. 52(4). 301–313. 45 indexed citations
13.
Nakamura, Keigo, Tomoki Toda, Hiroshi Saruwatari, & Kiyohiro Shikano. (2009). Electrolaryngeal speech enhancement based on statistical voice conversion. 1431–1434. 7 indexed citations
14.
Toda, Tomoki, et al.. (2009). Voice conversion for various types of body transmitted speech. 3601–3604. 24 indexed citations
15.
Nakamura, Keigo, et al.. (2009). Emphasized speech synthesis based on hidden Markov models. 76–81. 16 indexed citations
16.
Miyamoto, Daisuke, Keigo Nakamura, Tomoki Toda, Hiroshi Saruwatari, & Kiyohiro Shikano. (2009). Acoustic compensation methods for body transmitted speech conversion. 263. 3901–3904. 6 indexed citations
17.
Nakamura, Keigo, Tomoki Toda, Yoshitaka Nakajima, Hiroshi Saruwatari, & Kiyohiro Shikano. (2008). Evaluation of speaking-aid system with voice conversion for laryngectomees toward its use in practical environments. 2209–2212. 9 indexed citations
18.
Nakamura, Keigo, Tomoki Toda, Hiroshi Saruwatari, & Kiyohiro Shikano. (2007). A Speech Communication Aid System for Total Laryngectomees Using Voice Conversion of Body Transmitted Artificial Speech. 90(3). 780–787. 5 indexed citations
19.
Nakamura, Keigo, Tomoki Toda, Hiroshi Saruwatari, & Kiyohiro Shikano. (2007). Impact of various small sound source signals on voice conversion accuracy in speech communication aid for laryngectomees. 2517–2520. 3 indexed citations
20.
Nakamura, Keigo, Tomoki Toda, Hiroshi Saruwatari, & Kiyohiro Shikano. (2006). Speaking aid system for total laryngectomees using voice conversion of body transmitted artificial speech. paper 1839–Tue3CaP.11. 33 indexed citations

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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