Shigeki Karita

3.1k total citations · 2 hit papers
26 papers, 1.8k citations indexed

About

Shigeki Karita is a scholar working on Artificial Intelligence, Signal Processing and Control and Systems Engineering. According to data from OpenAlex, Shigeki Karita has authored 26 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 15 papers in Signal Processing and 2 papers in Control and Systems Engineering. Recurrent topics in Shigeki Karita's work include Speech Recognition and Synthesis (21 papers), Speech and Audio Processing (12 papers) and Natural Language Processing Techniques (12 papers). Shigeki Karita is often cited by papers focused on Speech Recognition and Synthesis (21 papers), Speech and Audio Processing (12 papers) and Natural Language Processing Techniques (12 papers). Shigeki Karita collaborates with scholars based in Japan, United States and United Kingdom. Shigeki Karita's co-authors include Shinji Watanabe, Tomoki Hayashi, Takaaki Hori, Nanxin Chen, Matthew Wiesner, Adithya Renduchintala, Tsubasa Ochiai, Yuya Unno, Jahn Heymann and Tomohiro Nakatani and has published in prestigious journals such as NTT technical review, arXiv (Cornell University) and ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

In The Last Decade

Shigeki Karita

23 papers receiving 1.6k citations

Hit Papers

ESPnet: End-to-End Speech Processing Toolkit 2018 2026 2020 2023 2018 2019 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. ESPnet: End-to-End Speech Processing Toolkit
    2018 848 citations Shinji Watanabe, Takaaki Hori et al. profile →
  2. A Comparative Study on Transformer vs RNN in Speech Applications
    2019 439 citations Shigeki Karita, Nanxin Chen et al. arXiv (Cornell University) profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shigeki Karita Japan 13 1.5k 1.1k 116 72 53 26 1.8k
Khe Chai Sim Singapore 21 1.6k 1.0× 1.2k 1.1× 157 1.4× 50 0.7× 31 0.6× 111 1.8k
Tom Ko China 13 1.6k 1.0× 1.3k 1.2× 167 1.4× 107 1.5× 31 0.6× 44 1.8k
Nanxin Chen United States 19 2.0k 1.3× 1.6k 1.5× 207 1.8× 86 1.2× 29 0.5× 29 2.3k
Chao Weng China 22 1.0k 0.7× 992 0.9× 209 1.8× 34 0.5× 88 1.7× 71 1.4k
Ignacio López Moreno United States 16 2.2k 1.4× 1.9k 1.8× 188 1.6× 61 0.8× 39 0.7× 56 2.5k
Joseph P. Campbell United States 22 1.4k 0.9× 1.4k 1.3× 252 2.2× 103 1.4× 43 0.8× 62 1.8k
Rohit Prabhavalkar United States 21 1.6k 1.1× 1.0k 1.0× 154 1.3× 36 0.5× 18 0.3× 59 1.8k
C. Wellekens France 12 842 0.6× 742 0.7× 180 1.6× 96 1.3× 43 0.8× 53 1.1k
Michiel Bacchiani United States 24 1.6k 1.0× 1.1k 1.0× 142 1.2× 74 1.0× 151 2.8× 66 1.9k
Gregory Sell United States 17 2.4k 1.6× 2.1k 1.9× 159 1.4× 122 1.7× 22 0.4× 38 2.6k

Countries citing papers authored by Shigeki Karita

Since Specialization
Citations

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

Fields of papers citing papers by Shigeki Karita

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shigeki Karita

This figure shows the co-authorship network connecting the top 25 collaborators of Shigeki Karita. A scholar is included among the top collaborators of Shigeki Karita 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 Shigeki Karita. Shigeki Karita 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.
Ma, Min, Yuma Koizumi, Shigeki Karita, et al.. (2024). FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks. 1835–1839. 1 indexed citations
3.
Koizumi, Yuma, Heiga Zen, Shigeki Karita, et al.. (2023). LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus. 5496–5500. 31 indexed citations
4.
5.
Koizumi, Yuma, Shigeki Karita, Arun Narayanan, Sankaran Panchapagesan, & Michiel Bacchiani. (2022). SNRi Target Training for Joint Speech Enhancement and Recognition. Interspeech 2022. 7 indexed citations
6.
Karita, Shigeki, et al.. (2022). Knowledge Transfer from Large-Scale Pretrained Language Models to End-To-End Speech Recognizers. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 8512–8516. 13 indexed citations
7.
Watanabe, Shinji, Xuankai Chang, Pengcheng Guo, et al.. (2021). The 2020 ESPnet Update: New Features, Broadened Applications, Performance Improvements, and Future Plans. 30. 1–6. 29 indexed citations
8.
Karita, Shigeki, Nanxin Chen, Tomoki Hayashi, et al.. (2019). A Comparative Study on Transformer vs RNN in Speech Applications. arXiv (Cornell University). 449–456. 439 indexed citations breakdown →
9.
Delcroix, Marc, Shinji Watanabe, Tsubasa Ochiai, et al.. (2019). End-to-End SpeakerBeam for Single Channel Target Speech Recognition. 451–455. 13 indexed citations
10.
Karita, Shigeki, Shinji Watanabe, Tomoharu Iwata, et al.. (2019). Semi-supervised End-to-end Speech Recognition Using Text-to-speech and Autoencoders. 6166–6170. 32 indexed citations
11.
12.
Ogawa, Atsunori, Marc Delcroix, Shigeki Karita, & Tomohiro Nakatani. (2018). Rescoring N-Best Speech Recognition List Based on One-on-One Hypothesis Comparison Using Encoder-Classifier Model. 6099–6103. 15 indexed citations
13.
Karita, Shigeki, Atsunori Ogawa, Marc Delcroix, & Tomohiro Nakatani. (2018). SEQUENCE TRAINING OF ENCODER-DECODER MODEL USING POLICY GRADIENT FOR END- TO-END SPEECH RECOGNITION. 12 indexed citations
14.
Karita, Shigeki, Shinji Watanabe, Tomoharu Iwata, Atsunori Ogawa, & Marc Delcroix. (2018). Semi-Supervised End-to-End Speech Recognition. 36 indexed citations
15.
Watanabe, Shinji, Takaaki Hori, Shigeki Karita, et al.. (2018). ESPnet: End-to-End Speech Processing Toolkit. 2207–2211. 848 indexed citations breakdown →
16.
Higuchi, Takuya, Keisuke Kinoshita, Nobutaka Ito, Shigeki Karita, & Tomohiro Nakatani. (2018). Frame-by-Frame Closed-Form Update for Mask-Based Adaptive MVDR Beamforming. 531–535. 33 indexed citations
17.
Delcroix, Marc, Keisuke Kinoshita, Atsunori Ogawa, et al.. (2017). Personalizing Your Speech Interface with Context Adaptive Deep Neural Networks. NTT technical review. 15(11). 35–39. 2 indexed citations
18.
Karita, Shigeki, Atsunori Ogawa, Marc Delcroix, & Tomohiro Nakatani. (2017). Forward-Backward Convolutional LSTM for Acoustic Modeling. 1601–1605. 4 indexed citations
19.
Yoshioka, Takuya, Shigeki Karita, & Tomohiro Nakatani. (2015). Far-field speech recognition using CNN-DNN-HMM with convolution in time. 4360–4364. 22 indexed citations
20.

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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