Takashi Masuko

10.8k total citations · 1 hit paper
248 papers, 7.2k citations indexed

About

Takashi Masuko is a scholar working on Molecular Biology, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Takashi Masuko has authored 248 papers receiving a total of 7.2k indexed citations (citations by other indexed papers that have themselves been cited), including 114 papers in Molecular Biology, 65 papers in Artificial Intelligence and 51 papers in Signal Processing. Recurrent topics in Takashi Masuko's work include Speech Recognition and Synthesis (63 papers), Speech and Audio Processing (48 papers) and Monoclonal and Polyclonal Antibodies Research (45 papers). Takashi Masuko is often cited by papers focused on Speech Recognition and Synthesis (63 papers), Speech and Audio Processing (48 papers) and Monoclonal and Polyclonal Antibodies Research (45 papers). Takashi Masuko collaborates with scholars based in Japan, United States and United Kingdom. Takashi Masuko's co-authors include Takao Kobayashi, Keiichi Tokuda, Tadashi Kitamura, Takayoshi Yoshimura, Kazuei Igarashi, Keiko Kashiwagi, Junichi Yamagishi, Keith Williams, Heiga Zen and Yoshiyuki Hashimoto and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and The Journal of Immunology.

In The Last Decade

Takashi Masuko

242 papers receiving 6.6k citations

Hit Papers

Speech parameter generation algorithms for HMM-based spee... 2002 2026 2010 2018 2002 200 400 600

Peers

Takashi Masuko
George C. Tseng United States
Shuyu Li China
Seán Murphy United States
Ting Qian United States
Henggui Zhang United Kingdom
Yun Lei China
I. Goldberg United States
Takashi Masuko
Citations per year, relative to Takashi Masuko Takashi Masuko (= 1×) peers Kazuhiro Kobayashi

Countries citing papers authored by Takashi Masuko

Since Specialization
Citations

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

Fields of papers citing papers by Takashi Masuko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takashi Masuko

This figure shows the co-authorship network connecting the top 25 collaborators of Takashi Masuko. A scholar is included among the top collaborators of Takashi Masuko 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 Takashi Masuko. Takashi Masuko 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
2.
Hara, Yuta, Shogo Okazaki, Yasutoshi Akiyama, et al.. (2023). Dual‐targeting therapy against HER3/MET in human colorectal cancers. Cancer Medicine. 12(8). 9684–9696. 4 indexed citations
3.
Udagawa, Hibiki, Monique B. Nilsson, Jacqulyne Robichaux, et al.. (2023). HER4 and EGFR Activate Cell Signaling in NRG1 Fusion-Driven Cancers: Implications for HER2-HER3-specific Versus Pan-HER Targeting Strategies. Journal of Thoracic Oncology. 19(1). 106–118. 9 indexed citations
4.
Hihara, Fukiko, Hiroki Matsumoto, Mitsuyoshi Yoshimoto, et al.. (2022). In Vitro Tumor Cell-Binding Assay to Select High-Binding Antibody and Predict Therapy Response for Personalized 64Cu-Intraperitoneal Radioimmunotherapy against Peritoneal Dissemination of Pancreatic Cancer: A Feasibility Study. International Journal of Molecular Sciences. 23(10). 5807–5807. 3 indexed citations
5.
Okazaki, Shogo, Kiyoko Umene, Juntaro Yamasaki, et al.. (2019). Glutaminolysis‐related genes determine sensitivity to xCT‐targeted therapy in head and neck squamous cell carcinoma. Cancer Science. 110(11). 3453–3463. 62 indexed citations
6.
Yoshikawa, Momoko, Kenji Tsuchihashi, Takatsugu Ishimoto, et al.. (2013). xCT Inhibition Depletes CD44v-Expressing Tumor Cells That Are Resistant to EGFR-Targeted Therapy in Head and Neck Squamous Cell Carcinoma. Cancer Research. 73(6). 1855–1866. 168 indexed citations
7.
Yae, Toshifumi, Kenji Tsuchihashi, Takatsugu Ishimoto, et al.. (2012). Alternative splicing of CD44 mRNA by ESRP1 enhances lung colonization of metastatic cancer cell. Nature Communications. 3(1). 883–883. 311 indexed citations
8.
Nose, Takashi, Junichi Yamagishi, Takashi Masuko, & Takao Kobayashi. (2007). A Style Control Technique for HMM-Based Expressive Speech Synthesis(Speech and Hearing). IEICE Transactions on Information and Systems. 90(9). 1406–1413. 4 indexed citations
9.
Tachibana, Makoto, Junichi Yamagishi, Takashi Masuko, & Takao Kobayashi. (2005). Speech Synthesis with Various Emotional Expressions and Speaking Styles by Style Interpolation and Morphing( Life-like Agent and its Communication). IEICE Transactions on Information and Systems. 88(11). 2484–2491. 5 indexed citations
10.
Masuko, Takashi, et al.. (2004). Robust F 0 Estimation of Speech Signal Using Harmonicity Measure Based on Instantaneous Frequency. IEICE Transactions on Information and Systems. 87(12). 2812–2820. 14 indexed citations
11.
Yamagishi, Junichi, et al.. (2003). A Training Method of Average Voice Model for HMM-Based Speech Synthesis. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 86(8). 1956–1963. 44 indexed citations
12.
Masuko, Takashi, et al.. (2003). A Study on Average Voice Model Training Using Vocal Tract Length Normalization. IEICE technical report. Speech. 103(27). 69–74. 6 indexed citations
13.
Yamagishi, Junichi, et al.. (2003). A Context Clustering Technique for Average Voice Models. IEICE Transactions on Information and Systems. 86(3). 534–542. 20 indexed citations
14.
Tokuda, Keiichi, Takashi Masuko, Noboru Miyazaki, & Takao Kobayashi. (2002). Multi-Space Probability Distribution HMM. IEICE Transactions on Information and Systems. 85(3). 455–464. 156 indexed citations
15.
Masuko, Takashi, et al.. (2002). HMM-Based Synthesis of Hand-Gesture Animation. 102(517). 43–48. 1 indexed citations
16.
Tokuda, Keiichi, et al.. (2001). Investigation of State Duration Model based on Gamma distribution for HMM-based Speech Synthesis. 101(353). 57–62. 10 indexed citations
17.
Koishida, Kazuhito, Keiichi Tokuda, Takashi Masuko, & Takao Kobayashi. (2001). Vector Quantization of Speech Spectral Parameters Using Statistics of Static and Dynamic Features. IEICE Transactions on Information and Systems. 84(10). 1427–1434. 8 indexed citations
18.
Miyajima, Chiyomi, et al.. (2001). Text-Independent Speaker Identification Using Gaussian Mixture Models Based on Multi-Space Probability Distribution. IEICE Transactions on Information and Systems. 84(7). 847–855. 17 indexed citations
19.
Masuko, Takashi, et al.. (1998). Visual Speech Synthesis Based on Parameter Generation From HMM: Speech-Driven and Text-And-Speech-Driven Approaches.. Tokyo Tech Research Repository (Tokyo Institute of Technology). 221–224. 22 indexed citations
20.
Masuko, Takashi, et al.. (1998). Speaker adaptation for HMM-based speech synthesis system using MLLR.. Tokyo Tech Research Repository (Tokyo Institute of Technology). 273–276. 71 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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