Leonardo Neumeyer
- Artificial Intelligence top 1%
- Signal Processing top 0.5%
- Computer Networks and Communications top 2%
- Information Systems top 2%
- Experimental and Cognitive Psychology top 5%
- Co-authors
- Vassilios DigalakisHoracio FrancoBruce RobbinsAnish NairM. WeintraubYoon KimP. PriceAnanth Sankar
- Topics
- Speech Recognition and Synthesis (26 papers)Speech and Audio Processing (21 papers)Music and Audio Processing (9 papers)
- Journals
- The Journal of the Acoustical Society of AmericaIEEE Journal on Selected Areas in CommunicationsIEEE Transactions on Speech and Audio Processing
- Partner nations
- United StatesGreeceSweden
In The Last Decade
Leonardo Neumeyer
35 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 1.4k
- Signal Processing 993
- Computer Networks and Communications 473
- Information Systems 413
- Experimental and Cognitive Psychology 278
Countries citing papers authored by Leonardo Neumeyer
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
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 of co-authors of Leonardo Neumeyer
This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Neumeyer. A scholar is included among the top collaborators of Leonardo Neumeyer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Leonardo Neumeyer. Leonardo Neumeyer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 15 | |
| 2 | 51 | |
| 3 | 11 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 6 | |
| 7 | Common speech/language issues | 0 |
| 8 | Multiple dialects and languages | 2 |
| 9 | Porting a recogniser to a new language | 4 |
| 10 | 90 | |
| 11 | Acoustic modelling | 4 |
| 12 | 13 | |
| 13 | 13 | |
| 14 | 4 | |
| 15 | 8 | |
| 16 | Spoken Language Translator: Phase Two Report | 4 |
| 17 | 43 | |
| 18 | 4 | |
| 19 | 287 | |
| 20 | 14 |
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) and Music and Audio Processing (9 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. Their work appears in journals such as The Journal of the Acoustical Society of America, IEEE Journal on Selected Areas in Communications and IEEE Transactions on Speech and Audio 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.