Nobukatsu Hojo

1.9k total citations
34 papers, 1.0k citations indexed

About

Nobukatsu Hojo is a scholar working on Signal Processing, Artificial Intelligence and Experimental and Cognitive Psychology. According to data from OpenAlex, Nobukatsu Hojo has authored 34 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Signal Processing, 26 papers in Artificial Intelligence and 3 papers in Experimental and Cognitive Psychology. Recurrent topics in Nobukatsu Hojo's work include Speech and Audio Processing (26 papers), Speech Recognition and Synthesis (24 papers) and Music and Audio Processing (17 papers). Nobukatsu Hojo is often cited by papers focused on Speech and Audio Processing (26 papers), Speech Recognition and Synthesis (24 papers) and Music and Audio Processing (17 papers). Nobukatsu Hojo collaborates with scholars based in Japan. Nobukatsu Hojo's co-authors include Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka, Yusuke Ijima, Kunio Kashino, Kaoru Hiramatsu, Masanobu Abe, Tomoki Toda, Hiroyasu Ando and Wen-Chin Huang and has published in prestigious journals such as Speech Communication, IEEE/ACM Transactions on Audio Speech and Language Processing and IEICE Transactions on Information and Systems.

In The Last Decade

Nobukatsu Hojo

30 papers receiving 958 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nobukatsu Hojo Japan 16 845 815 168 72 30 34 1.0k
Kou Tanaka Japan 14 779 0.9× 777 1.0× 166 1.0× 112 1.6× 26 0.9× 45 961
Adithya Renduchintala United States 6 855 1.0× 544 0.7× 89 0.5× 24 0.3× 45 1.5× 17 934
Cheng-I Lai United States 10 718 0.8× 567 0.7× 104 0.6× 29 0.4× 80 2.7× 14 875
Cemal Hanilçi Türkiye 13 935 1.1× 1.0k 1.3× 278 1.7× 119 1.7× 19 0.6× 39 1.2k
Yoshihiko Nankaku Japan 17 1.1k 1.3× 998 1.2× 209 1.2× 29 0.4× 109 3.6× 117 1.3k
Vimal Manohar United States 15 1.0k 1.2× 749 0.9× 49 0.3× 28 0.4× 48 1.6× 28 1.1k
Yuya Unno United States 4 789 0.9× 543 0.7× 65 0.4× 24 0.3× 43 1.4× 5 863
Shinnosuke Takamichi Japan 16 756 0.9× 682 0.8× 115 0.7× 33 0.5× 64 2.1× 116 978
Jenthe Thienpondt Belgium 6 792 0.9× 704 0.9× 75 0.4× 32 0.4× 42 1.4× 13 894
Chunlei Zhang United States 15 595 0.7× 573 0.7× 52 0.3× 33 0.5× 24 0.8× 39 718

Countries citing papers authored by Nobukatsu Hojo

Since Specialization
Citations

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

Fields of papers citing papers by Nobukatsu Hojo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nobukatsu Hojo

This figure shows the co-authorship network connecting the top 25 collaborators of Nobukatsu Hojo. A scholar is included among the top collaborators of Nobukatsu Hojo 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 Nobukatsu Hojo. Nobukatsu Hojo 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.
2.
Hojo, Nobukatsu, et al.. (2025). Data stream-pairwise bottleneck transformer for engagement estimation from video conversation. Frontiers in Artificial Intelligence. 8. 1516295–1516295.
3.
Masumura, Ryo, et al.. (2025). Multimodal Fine-Grained Apparent Personality Trait Recognition: Joint Modeling of Big Five and Questionnaire Item-level Scores. Proceedings of the AAAI Conference on Artificial Intelligence. 39(2). 1456–1464.
4.
Kameoka, Hirokazu, et al.. (2024). VoiceGrad: Non-Parallel Any-to-Many Voice Conversion With Annealed Langevin Dynamics. IEEE/ACM Transactions on Audio Speech and Language Processing. 32. 2213–2226. 3 indexed citations
5.
Hojo, Nobukatsu, et al.. (2024). Participant-Pair-Wise Bottleneck Transformer for Engagement Estimation from Video Conversation. 4079–4083. 1 indexed citations
6.
Masumura, Ryo, Yoshihiko Yamazaki, Hiroshi Sato, et al.. (2023). End-to-End Joint Target and Non-Target Speakers ASR. 2903–2907. 1 indexed citations
7.
Satō, Hiroshi, Ryo Masumura, Tsubasa Ochiai, et al.. (2023). Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss. 854–858. 4 indexed citations
8.
Sato, Hiroshi, et al.. (2023). Transcribing Speech as Spoken and Written Dual Text Using an Autoregressive Model. 461–465. 1 indexed citations
9.
Kaneko, Takuhiro, Hirokazu Kameoka, Kou Tanaka, & Nobukatsu Hojo. (2021). Maskcyclegan-VC: Learning Non-Parallel Voice Conversion with Filling in Frames. 5919–5923. 40 indexed citations
10.
Kameoka, Hirokazu, et al.. (2020). ConvS2S-VC: Fully Convolutional Sequence-to-Sequence Voice Conversion. IEEE/ACM Transactions on Audio Speech and Language Processing. 28. 1849–1863. 39 indexed citations
11.
Kaneko, Takuhiro, Hirokazu Kameoka, Kou Tanaka, & Nobukatsu Hojo. (2020). CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-Spectrogram Conversion. 2017–2021. 55 indexed citations
12.
Kameoka, Hirokazu, Takuhiro Kaneko, Kou Tanaka, & Nobukatsu Hojo. (2020). Nonparallel Voice Conversion With Augmented Classifier Star Generative Adversarial Networks. IEEE/ACM Transactions on Audio Speech and Language Processing. 28. 2982–2995. 17 indexed citations
13.
Tanaka, Kou, Takuhiro Kaneko, Nobukatsu Hojo, & Hirokazu Kameoka. (2020). Communication with Desired Voice. NTT technical review. 18(11). 27–31. 1 indexed citations
14.
Abe, Masanobu, et al.. (2020). Model architectures to extrapolate emotional expressions in DNN-based text-to-speech. Speech Communication. 126. 35–43. 12 indexed citations
15.
Kaneko, Takuhiro, Hirokazu Kameoka, Kou Tanaka, & Nobukatsu Hojo. (2019). StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion. 679–683. 91 indexed citations
16.
Kameoka, Hirokazu, Takuhiro Kaneko, Kou Tanaka, & Nobukatsu Hojo. (2018). StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks. 266–273. 208 indexed citations
17.
Hojo, Nobukatsu, Hirokazu Kameoka, Kou Tanaka, & Takuhiro Kaneko. (2018). Automatic Speech Pronunciation Correction with Dynamic Frequency Warping-Based Spectral Conversion. 2310–2314. 1 indexed citations
18.
Ijima, Yusuke, Nobukatsu Hojo, Ryo Masumura, & Taichi Asami. (2017). Prosody Aware Word-Level Encoder Based on BLSTM-RNNs for DNN-Based Speech Synthesis. 764–768. 2 indexed citations
19.
Kaneko, Takuhiro, et al.. (2016). Generative Adversarial Network-based Postfiltering for Statistical Parametric Speech Synthesis. IEICE Technical Report; IEICE Tech. Rep.. 116(378). 89–94. 2 indexed citations
20.
Hojo, Nobukatsu, et al.. (2016). An Investigation of DNN-Based Speech Synthesis Using Speaker Codes. 2278–2282. 26 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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