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.
Recurrent neural network based language model
20103.3k citationsTomáš Mikolov, Martin Karafiát et al.profile →
Extensions of recurrent neural network language model
2011929 citationsTomáš Mikolov, Stefan Kombrink et al.profile →
Strategies for training large scale neural network language models
2011329 citationsTomáš Mikolov, Anoop Deoras et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Jaň Černocký'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 Jaň Černocký with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jaň Černocký more than expected).
This network shows the impact of papers produced by Jaň Černocký. 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 Jaň Černocký. The network helps show where Jaň Černocký may publish in the future.
Co-authorship network of co-authors of Jaň Černocký
This figure shows the co-authorship network connecting the top 25 collaborators of Jaň Černocký.
A scholar is included among the top collaborators of Jaň Černocký 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 Jaň Černocký. Jaň Černocký is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Plchot, Oldřich, Martin Karafiát, Niko Brümmer, et al.. (2012). Speaker vectors from subspace Gaussian mixture model as complementary features for language identification.. 330–333.4 indexed citations
13.
D’Haro, Luis Fernando, Ondřej Glembek, Oldřich Plchot, et al.. (2012). Phonotactic language recognition using i-vectors and phoneme posteriogram counts. Conference of the International Speech Communication Association. 42–45.25 indexed citations
14.
Mikolov, Tomáš, Anoop Deoras, Daniel Povey, Lukáš Burget, & Jaň Černocký. (2011). Strategies for training large scale neural network language models. 196–201.329 indexed citations breakdown →
Plchot, Oldřich, Niko Brümmer, Lukáš Burget, et al.. (2010). Data selection and calibration issues in automatic language recognition - investigation with BUT-AGNITIO NIST LRE 2009 system.. 37.21 indexed citations
18.
Černocký, Jaň, et al.. (2009). Audio Surveillance through Known Event Classification.5 indexed citations
Černocký, Jaň, Jérôme Boudy, Khalid Choukri, et al.. (2000). SpeechDat(E) - Eastern European Telephone Speech Databases. Language Resources and Evaluation. 20–25.23 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.