Michael Levit

669 citations
34 papers · 507 · h-index 10

Impact in

Papers in

    • Natural Language Processing Techniques 17
    • Topic Modeling 13
    • Speech and dialogue systems 11
    • Speech Recognition and Synthesis 11
    • Text and Document Classification Technologies 3
    • Music and Audio Processing 4
    • Speech and Audio Processing 3

Michael Levit

32 papers receiving 456 citations

Peers

Michael Levit
Comparison fields: 5 of 81
  • Experimental and Cognitive Psychology 96
  • Artificial Intelligence 203
  • Signal Processing 65
  • Developmental and Educational Psychology 59
  • Biophysics 22
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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 200686
2 200676
3
The Human Speechome Project
200656
4 199642
5 201138
6 199636
7 200731
8 201322
9
Use of prosodic speech characteristics for automated detection of alcohol intoxication
200120
10 201410
11 20099
12 19948
13 20078
14 19967
15 20126
16 20036
17 20026
18 20076
19 20016
20 20075

About Michael Levit

Michael Levit is a scholar working on Artificial Intelligence, Signal Processing, Mechanical Engineering, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering, having authored 34 papers that have together received 507 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (17 papers), Topic Modeling (13 papers), Speech and dialogue systems (11 papers), Speech Recognition and Synthesis (11 papers), Music and Audio Processing (4 papers), Semiconductor materials and interfaces (3 papers), Text and Document Classification Technologies (3 papers) and Speech and Audio Processing (3 papers). The work is most often cited by research in Experimental and Cognitive Psychology (96 citations), Artificial Intelligence (203 citations), Signal Processing (65 citations), Developmental and Educational Psychology (59 citations) and Biophysics (22 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, Nikolaos Mavridis, Rony Kubat and Rupal Patel. Their work appears in journals such as Journal of Applied Physics, Computer Speech & Language, Review of Scientific Instruments, Applied Physics Letters and Materials Letters.

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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