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.
Audio-visual speech recognition using deep learning
2014404 citationsKazuhiro Nakadai, Hiroshi G. Okuno et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Kazuhiro Nakadai
Since
Specialization
Citations
This map shows the geographic impact of Kazuhiro Nakadai'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 Kazuhiro Nakadai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kazuhiro Nakadai more than expected).
Fields of papers citing papers by Kazuhiro Nakadai
This network shows the impact of papers produced by Kazuhiro Nakadai. 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 Kazuhiro Nakadai. The network helps show where Kazuhiro Nakadai may publish in the future.
Co-authorship network of co-authors of Kazuhiro Nakadai
This figure shows the co-authorship network connecting the top 25 collaborators of Kazuhiro Nakadai.
A scholar is included among the top collaborators of Kazuhiro Nakadai 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 Kazuhiro Nakadai. Kazuhiro Nakadai is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Brock, Heike & Kazuhiro Nakadai. (2018). Deep JSLC: A Multimodal Corpus Collection for Data-driven Generation of Japanese Sign Language Expressions. Language Resources and Evaluation.9 indexed citations
Lubis, Nurul, Randy Gómez, Sakriani Sakti, et al.. (2016). Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition. Language Resources and Evaluation. 2180–2184.3 indexed citations
Nakadai, Kazuhiro, Daisuke Matsuura, Hiroshi G. Okuno, & Hiroshi Tsujino. (2003). Improvement of three simultaneous speech recognition by using AV integration and scattering theory for humanoid.. AVSP. 157–162.1 indexed citations
Lourens, Tino, Kazuhiro Nakadai, Hiroshi G. Okuno, & Hiroaki Kitano. (2001). Graph Extraction from Color Images. The European Symposium on Artificial Neural Networks. 329–334.3 indexed citations
19.
Nakadai, Kazuhiro, Tino Lourens, Hiroshi G. Okuno, & Hiroaki Kitano. (2000). Active Audition for Humanoid. National Conference on Artificial Intelligence. 832–839.160 indexed citations
20.
Kashino, Kunio, et al.. (1998). Application of the Bayesian probability network to music scene analysis. 115–137.65 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.