Leonardo Neumeyer

2.8k citations
36 papers · 1.9k indexed · 1 hit paper · h-index 16

Leonardo Neumeyer

35 papers receiving 1.7k citations

Hit Papers

S4: Distributed Stream Computing Platform5932010202620152020100200300400500

Peers

Leonardo Neumeyer
Comparison fields: 5 of 67
  • Signal Processing 993
  • Artificial Intelligence 1.4k
  • Experimental and Cognitive Psychology 278
  • Computer Networks and Communications 473
  • Information Systems 413
Replace Zongheng Yang with:
Zongheng Yang United States
Angela Fan United States
M.A. Zissman United States
Yuting Chen China
Donald E. Porter United States
Rui Xia China
Claudia Picardi Italy
Francesco Bergadano Italy
Francis Kubala United States
Paul Lamere United States
Leonardo Neumeyer relative to Zongheng Yang United States Zongheng Yang's profile →
Citations per field
00.5×10×16.3×
Zongheng Yang · 1×
Citations per year

Countries citing papers authored by Leonardo Neumeyer

Since Specialization
Citations

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

Fields of papers citing papers by Leonardo Neumeyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 23 scholars most cited alongside Leonardo Neumeyer, 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 Leonardo Neumeyer Line = papers co-authored together Leonardo Neumeyer links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 200215
2 200251
3 200211
4 20022
5 20024
6 20026
7
Common speech/language issues
20000
8
Multiple dialects and languages
20002
9
Porting a recogniser to a new language
20004
10 200090
11
Acoustic modelling
20004
12 199913
13 199913
14 19984
15 19988
16
Spoken Language Translator: Phase Two Report
19974
17 199743
18 19964
19 1995287
20 199414

About Leonardo Neumeyer

Leonardo Neumeyer is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 36 papers that have together received 1.9k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (26 papers), Speech and Audio Processing (21 papers), Music and Audio Processing (9 papers), Natural Language Processing Techniques (8 papers), Advanced Data Compression Techniques (7 papers), Speech and dialogue systems (6 papers), Phonetics and Phonology Research (3 papers) and Bayesian Methods and Mixture Models (2 papers). The work is most often cited by research in Signal Processing (993 citations), Artificial Intelligence (1.4k citations) and Experimental and Cognitive Psychology (278 citations). Leonardo Neumeyer has collaborated with scholars based in United States, Greece and Sweden. Frequent co-authors include Vassilios Digalakis, Horacio Franco, Bruce Robbins, Anish Nair, M. Weintraub, Yoon Kim, P. Price, Ananth Sankar, Harry Bratt and Fuliang Weng.

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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