Ingo Siegert

755 citations
63 papers · 309 · h-index 10

Impact in

Papers in

Ingo Siegert

55 papers receiving 292 citations

Peers

Ingo Siegert
Comparison fields: 5 of 63
  • Experimental and Cognitive Psychology 166
  • Signal Processing 95
  • Artificial Intelligence 168
  • Human-Computer Interaction 21
  • Social Psychology 50
Replace Ronald Böck with:
Ronald Böck Germany
Rajdip Dhillon United States
Kun-Yi Huang Taiwan
Éva Székely Sweden
Sathish Pammi Germany
Volker Strom United Kingdom
Georgios Paraskevopoulos Greece
Christoph Draxler Germany
Bela Usabaev Germany
Najmeh Sadoughi United States
Ingo Siegert relative to Ronald Böck Germany Ronald Böck's profile →
Citations per field
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Citations per year

Countries citing papers authored by Ingo Siegert

Since Specialization
Citations

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

Fields of papers citing papers by Ingo Siegert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ingo Siegert, 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 Ingo Siegert Line = papers co-authored together Ingo Siegert links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 63 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201338
2 201121
3 201119
4 201814
5 201413
6 201311
7 201310
8 201510
9
Towards Emotion and Affect Detection in the Multimodal LAST MINUTE Corpus
20129
10 20219
11 20199
12 20128
13 20198
14
Measuring the Impact of Audio Compression on the Spectral Quality of Speech Data
20168
15
Comparing phonetic changes in computer-directed and human-directed speech
20197
16 20127
17 20137
18 20136
19 20205
20 20145

About Ingo Siegert

Ingo Siegert is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing, Social Psychology and Computer Vision and Pattern Recognition, having authored 63 papers that have together received 309 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (25 papers), Speech and dialogue systems (19 papers), Speech Recognition and Synthesis (17 papers), Speech and Audio Processing (13 papers), Social Robot Interaction and HRI (10 papers), AI in Service Interactions (7 papers), Music and Audio Processing (6 papers) and Topic Modeling (6 papers). The work is most often cited by research in Experimental and Cognitive Psychology (166 citations), Signal Processing (95 citations), Artificial Intelligence (168 citations), Human-Computer Interaction (21 citations) and Social Psychology (50 citations). Ingo Siegert has collaborated with scholars based in Germany, Switzerland and Russia. Frequent co-authors include Andreas Wendemuth, Ronald Böck, Bogdan Vlasenko, Oliver Niebuhr, Bernd Möbius, Ingmar Steiner, Wolfgang Minker, Alexey Karpov, Jörg Frommer and Matthias Haase. Their work appears in journals such as Language Resources and Evaluation, Frontiers in Communication, Cognitive Computation, Computer Speech & Language and Cognitive Systems Research.

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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