Matthias Eichner

430 citations
28 papers · 373 · h-index 10

Impact in

Papers in

    • Speech and Audio Processing 10
    • Music and Audio Processing 5
    • Speech Recognition and Synthesis 18
    • Speech and dialogue systems 11
    • Natural Language Processing Techniques 7

Matthias Eichner

28 papers receiving 349 citations

Peers

Matthias Eichner
Comparison fields: 5 of 79
  • Signal Processing 86
  • Computer Vision and Pattern Recognition 163
  • Artificial Intelligence 168
  • Human-Computer Interaction 21
  • Experimental and Cognitive Psychology 29
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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 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1
British Machine Vision Conference
2009160
2 200021
3 200420
4 200418
5 200717
6 200414
7 200613
8 200712
9 200211
10 200411
11 20029
12 19987
13 20076
14 20216
15 20046
16 20025
17 20045
18
Creating an individual speech rhythm: a data driven approach.
19985
19 20045
20 20044

About Matthias Eichner

Matthias Eichner is a scholar working on Signal Processing, Artificial Intelligence, Experimental and Cognitive Psychology, Computer Vision and Pattern Recognition and Philosophy, having authored 28 papers that have together received 373 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (18 papers), Speech and dialogue systems (11 papers), Speech and Audio Processing (10 papers), Natural Language Processing Techniques (7 papers), Phonetics and Phonology Research (5 papers), Music and Audio Processing (5 papers), Advanced Data Compression Techniques (2 papers) and Hermeneutics and Narrative Identity (2 papers). The work is most often cited by research in Signal Processing (86 citations), Computer Vision and Pattern Recognition (163 citations), Artificial Intelligence (168 citations), Human-Computer Interaction (21 citations) and Experimental and Cognitive Psychology (29 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 F Hoffmann, Steffen Werner, Oliver Jokisch, Rüdiger Hoffmann, Felix Schaeffler, Hussein Hussein and Janet Mackenzie Beck. Their work appears in journals such as IEEE Transactions on Speech and Audio Processing, Linguistics Vanguard, PRESENCE Virtual and Augmented Reality, Lecture notes in computer science and IEEE International Conference on Acoustics Speech and Signal Processing.

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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