Daniel Hládek

536 citations
50 papers · 295 indexed · h-index 9

Daniel Hládek

47 papers receiving 271 citations

Peers

Daniel Hládek
Comparison fields: 5 of 58
  • Artificial Intelligence 194
  • Computer Science Applications 28
  • Signal Processing 30
  • Health Informatics 3
  • Control and Systems Engineering 51
Replace Adrian Calma with:
Adrian Calma Germany
Oleksiy Khriyenko Finland
Amel Bouzeghoub France
Sachio Saiki Japan
Jim Glass United States
Yao-Chung Fan Taiwan
Srinivasan Janarthanam United Kingdom
Tengfei Shi China
Dilek Hakkani-Tür United States
A. El Saddik Canada
Daniel Hládek relative to Adrian Calma Germany Adrian Calma's profile →
Citations per field
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Adrian Calma · 1×
Citations per year

Countries citing papers authored by Daniel Hládek

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Hládek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 11 scholars most cited alongside Daniel Hládek, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Hládek Line = papers co-authored together Daniel Hládek links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20241
3 20241
4 20230
5 20232
6 20230
7 20231
8 20233
9 20222
10 202045
11
Building of children speech corpus for improving automatic subtitling services
20191
12 201945
13 20193
14 20182
15 20173
16
Evaluation Set for Slovak News Information Retrieval.
20161
17 201611
18
The Slovak Categorized News Corpus
20145
19
TUKE at MediaEval 2013 Spoken Web Search Task.
20135
20
Dagger: The Slovak morphological classifier
20128

About Daniel Hládek

Daniel Hládek is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computer Graphics and Computer-Aided Design and Control and Systems Engineering, having authored 50 papers that have together received 295 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (27 papers), Topic Modeling (15 papers), Speech Recognition and Synthesis (15 papers), Speech and dialogue systems (14 papers), Robotics and Automated Systems (6 papers), Robotic Path Planning Algorithms (4 papers), Sentiment Analysis and Opinion Mining (3 papers) and Fuzzy Logic and Control Systems (3 papers). The work is most often cited by research in Artificial Intelligence (194 citations), Computer Science Applications (28 citations), Signal Processing (30 citations), Health Informatics (3 citations) and Control and Systems Engineering (51 citations). Daniel Hládek has collaborated with scholars based in Slovakia, Taiwan and Hungary. Frequent co-authors include Ján Staš, Matúš Pleva, Stanislav Ondáš, Jozef Juhár, Ján Vaščák, Peter Sinčák, Patrick Bours, László Kovács, Ming-Hsiang Su and Julius Zimmermann. Their work appears in journals such as Electronics, IEEE Access, Language Resources and Evaluation, Multimedia Tools and Applications and Journal of Linguistics/Jazykovedný casopis.

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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