Michael Levit

674 citations
34 papers · 384 indexed · h-index 9

Impact in

Papers in

    • Natural Language Processing Techniques 17
    • Topic Modeling 13
    • Speech Recognition and Synthesis 11
    • Speech and dialogue systems 10
    • Text and Document Classification Technologies 3

Michael Levit

31 papers receiving 351 citations

Peers

Michael Levit
Comparison fields: 5 of 75
  • Experimental and Cognitive Psychology 77
  • Artificial Intelligence 172
  • Signal Processing 55
  • Biophysics 23
  • Ceramics and Composites 21
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Masashi Ito Japan
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Citations per field
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Citations per year

Countries citing papers authored by Michael Levit

Since Specialization
Citations

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

Fields of papers citing papers by Michael Levit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200669
2
The Human Speechome Project
200648
3 199640
4 201138
5 199634
6 200730
7 201320
8
Use of prosodic speech characteristics for automated detection of alcohol intoxication
200116
9 20149
10 19948
11 20077
12 19967
13 20026
14 20016
15 20075
16 20075
17 20125
18 19994
19 20034
20 20094

About Michael Levit

Michael Levit is a scholar working on Artificial Intelligence, Structural Biology, Signal Processing, Biophysics and General Materials Science, having authored 34 papers that have together received 384 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (17 papers), Topic Modeling (13 papers), Speech Recognition and Synthesis (11 papers), Speech and dialogue systems (10 papers), Music and Audio Processing (4 papers), Text and Document Classification Technologies (3 papers), Speech and Audio Processing (3 papers) and Semiconductor materials and interfaces (3 papers). The work is most often cited by research in Experimental and Cognitive Psychology (77 citations), Artificial Intelligence (172 citations), Signal Processing (55 citations), Biophysics (23 citations) and Ceramics and Composites (21 citations). Michael Levit has collaborated with scholars based in United States, Israel and Germany. Frequent co-authors include B. Z. Weiss, I. Grimberg, Deb Roy, Elmar Nöth, Anton Batliner, H. Niemann, Stefan Steidl, Aharon Blank, Allen L. Gorin and Shuangyu Chang. Their work appears in journals such as Journal of Applied Physics, Journal of Magnetic Resonance, Computer Speech & Language, ACS Photonics and Review of Scientific Instruments.

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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