Matthias Eichner

429 citations
25 papers · 299 · h-index 9

Impact in

Papers in

    • Speech Recognition and Synthesis 16
    • Speech and dialogue systems 10
    • Natural Language Processing Techniques 6
    • Speech and Audio Processing 8
    • Music and Audio Processing 4

Matthias Eichner

25 papers receiving 279 citations

Peers

Matthias Eichner
Comparison fields: 5 of 73
  • Computer Vision and Pattern Recognition 137
  • Signal Processing 59
  • Artificial Intelligence 129
  • Human-Computer Interaction 17
  • Experimental and Cognitive Psychology 26
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Citations per field
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Citations per year

Countries citing papers authored by Matthias Eichner

Since Specialization
Citations

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

Fields of papers citing papers by Matthias Eichner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside Matthias Eichner, 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 Matthias Eichner Line = papers co-authored together Matthias Eichner links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1
British Machine Vision Conference
2009137
2 200019
3 200418
4 200415
5 200412
6 200210
7 200710
8 20069
9 20028
10 20048
11
Creating an individual speech rhythm: a data driven approach.
19985
12 20215
13 20025
14 20045
15 20045
16 19985
17
Measuring the Quality of Pronunciation Dictionaries
20084
18 20074
19 20043
20 20053

About Matthias Eichner

Matthias Eichner is a scholar working on Artificial Intelligence, Signal Processing, Experimental and Cognitive Psychology, Computer Vision and Pattern Recognition and General Health Professions, having authored 25 papers that have together received 299 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (16 papers), Speech and dialogue systems (10 papers), Speech and Audio Processing (8 papers), Natural Language Processing Techniques (6 papers), Phonetics and Phonology Research (5 papers), Music and Audio Processing (4 papers), Hermeneutics and Narrative Identity (2 papers) and Advanced Data Compression Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (137 citations), Signal Processing (59 citations), Artificial Intelligence (129 citations), Human-Computer Interaction (17 citations) and Experimental and Cognitive Psychology (26 citations). Matthias Eichner has collaborated with scholars based in Germany, Venezuela and United Kingdom. Frequent co-authors include Vittorio Ferrari, Matthias Wolff, Rüdiger Hoffmann, Robert Hoffmann, Steffen Werner, Oliver Jokisch, Rüdiger Hoffmann, Hussein Hussein, Janet Mackenzie Beck and Felix Schaeffler. Their work appears in journals such as PRESENCE Virtual and Augmented Reality, IEEE Transactions on Speech and Audio Processing, Linguistics Vanguard, SSW and Queen Margaret University Publications Repository (Queen Margaret University).

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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