Tomoko Matsui

2.8k total citations
133 papers, 1.5k citations indexed

About

Tomoko Matsui is a scholar working on Artificial Intelligence, Signal Processing and Developmental and Educational Psychology. According to data from OpenAlex, Tomoko Matsui has authored 133 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Artificial Intelligence, 47 papers in Signal Processing and 17 papers in Developmental and Educational Psychology. Recurrent topics in Tomoko Matsui's work include Speech Recognition and Synthesis (55 papers), Speech and Audio Processing (40 papers) and Music and Audio Processing (27 papers). Tomoko Matsui is often cited by papers focused on Speech Recognition and Synthesis (55 papers), Speech and Audio Processing (40 papers) and Music and Audio Processing (27 papers). Tomoko Matsui collaborates with scholars based in Japan, France and United Kingdom. Tomoko Matsui's co-authors include Sadaoki Furui, Konstantin Markov, Satoshi Nakamura, Tatsuya Kawahara, Yui Miura, Gareth W. Peters, François Septier, Daisuke Murakami, Masashi Sugiyama and Tor André Myrvoll and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Signal Processing and Journal of the American Geriatrics Society.

In The Last Decade

Tomoko Matsui

120 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tomoko Matsui Japan 23 860 743 223 186 178 133 1.5k
Shrikanth Narayanan United States 22 979 1.1× 1.0k 1.4× 72 0.3× 319 1.7× 167 0.9× 70 2.2k
Gérard Bailly France 23 767 0.9× 672 0.9× 153 0.7× 332 1.8× 221 1.2× 130 1.6k
Mikko Kurimo Finland 25 2.1k 2.4× 747 1.0× 68 0.3× 271 1.5× 143 0.8× 223 2.6k
Brian Mak Hong Kong 19 1.1k 1.3× 1.0k 1.4× 90 0.4× 261 1.4× 104 0.6× 115 1.6k
Keikichi Hirose Japan 19 1.6k 1.9× 1.0k 1.4× 83 0.4× 135 0.7× 95 0.5× 288 2.1k
Peter Bell United Kingdom 20 1.1k 1.3× 677 0.9× 67 0.3× 123 0.7× 94 0.5× 105 1.5k
Bryan Pellom United States 25 1.2k 1.4× 785 1.1× 80 0.4× 220 1.2× 99 0.6× 61 1.7k
Athanasios Katsamanis Greece 20 661 0.8× 667 0.9× 78 0.3× 357 1.9× 153 0.9× 68 1.5k
Alexey Karpov Russia 17 638 0.7× 479 0.6× 57 0.3× 213 1.1× 105 0.6× 126 1.2k
Victor W. Zue United States 27 2.8k 3.3× 1.4k 1.9× 76 0.3× 296 1.6× 176 1.0× 169 3.5k

Countries citing papers authored by Tomoko Matsui

Since Specialization
Citations

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

Fields of papers citing papers by Tomoko Matsui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomoko Matsui

This figure shows the co-authorship network connecting the top 25 collaborators of Tomoko Matsui. A scholar is included among the top collaborators of Tomoko Matsui 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 Tomoko Matsui. Tomoko Matsui 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.
Tran, Vu, François Septier, Daisuke Murakami, & Tomoko Matsui. (2024). Spatial–Temporal Temperature Forecasting Using Deep-Neural-Network-Based Domain Adaptation. Atmosphere. 15(1). 90–90. 1 indexed citations
2.
Peters, Gareth W., et al.. (2023). A Dynamic Stochastic Integrated Climate–Economic Spatiotemporal Model for Agricultural Insurance Products. North American Actuarial Journal. 28(1). 27–56.
3.
Matsui, Tomoko, et al.. (2023). An Observational Study of Google Reviews and Ratings of Medical Institutions. An Official Journal of the Japan Primary Care Association. 46(1). 2–11.
4.
Murakami, Daisuke & Tomoko Matsui. (2022). Improved log-Gaussian approximation for over-dispersed Poisson regression: Application to spatial analysis of COVID-19. PLoS ONE. 17(1). e0260836–e0260836. 5 indexed citations
5.
Shevchenko, Pavel V., Daisuke Murakami, Tomoko Matsui, & Tor André Myrvoll. (2021). Impact of COVID-19 type events on the economy and climate under the stochastic DICE model. arXiv (Cornell University). 2 indexed citations
7.
Oi, Manabu, Yoko Kamio, Yuko Yoshimura, et al.. (2017). Quantitative Aspects of Communicative Impairment Ascertained in a Large National Survey of Japanese Children. Journal of Autism and Developmental Disorders. 47(10). 3040–3048. 4 indexed citations
8.
Nishikawa, Tsuyoki, et al.. (2015). Study on i-vector based speaker identification for short utterances. IEICE Technical Report; IEICE Tech. Rep.. 115(99). 65–70. 1 indexed citations
9.
Higuchi, Tomoyuki, et al.. (2015). Road condition classification using a new global alignment kernel. 1–6. 3 indexed citations
10.
Markov, Konstantin & Tomoko Matsui. (2014). Dynamic Music Emotion Recognition Using State-Space Models. MediaEval. 3 indexed citations
11.
Markov, Konstantin, et al.. (2013). Music emotion recognition using Gaussian Processes. MediaEval. 7 indexed citations
12.
Ogawa, Tetsuji & Tomoko Matsui. (2013). Machine learning for speaker recognition. Nippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo Gakkaishi. 69(7). 349–356. 1 indexed citations
13.
Akiba, Tomoyosi, Hiromitsu Nishizaki, Kiyoaki Aikawa, Tatsuya Kawahara, & Tomoko Matsui. (2012). Designing an Evaluation Framework for Spoken Term Detection and Spoken Document Retrieval at the NTCIR-9 SpokenDoc Task. Language Resources and Evaluation. 3527–3534. 4 indexed citations
14.
Akiba, Tomoyosi, Hiromitsu Nishizaki, Kiyoaki Aikawa, Tatsuya Kawahara, & Tomoko Matsui. (2011). Overview of the IR for Spoken Documents Task in NTCIR-9 Workshop. NTCIR. 40 indexed citations
15.
Le, Duy-Dinh, et al.. (2007). NII-ISM, Japan at TRECVID 2007: High Level Feature Extraction. TRECVID. 4 indexed citations
16.
Matsui, Tomoko & Kunio Tanabe. (2006). Comparative Study of Speaker Identification Methods : dPLRM, SVM and GMM(Speaker Recognition, Statistical Modeling for Speech Processing). IEICE Transactions on Information and Systems. 89(3). 1066–1073. 1 indexed citations
17.
Lane, Ian, Tatsuya Kawahara, Tomoko Matsui, & Satoshi Nakamura. (2005). Dialogue Speech Recognition by Combining Hierarchical Topic Classification and Language Model Switching(Spoken Language Systems, Corpus-Based Speech Technologies). IEICE Transactions on Information and Systems. 88(3). 446–453. 1 indexed citations
18.
Matsui, Tomoko, et al.. (2004). Automatic Generation of Non-uniform HMM Topologies Based on the MDL Criterion. IEICE Transactions on Information and Systems. 87(8). 2121–2129. 27 indexed citations
19.
Matsui, Tomoko & Kiyoaki Aikawa. (2003). Robust modelfor speaker verification against session-dependent utterance variation. IEICE Transactions on Information and Systems. 86(4). 712–718. 1 indexed citations
20.
Zhang, Jinsong, Konstantin Markov, Tomoko Matsui, & Satoshi Nakamura. (2003). A Study on Acoustic Modeling of Pauses for Recongnizing Noisy Conversational Speech. IEICE Transactions on Information and Systems. 86(3). 489–496. 2 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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