Jaime Lorenzo-Trueba
- Artificial Intelligence top 2%
- Signal Processing top 2%
- Computer Vision and Pattern Recognition top 10%
- Experimental and Cognitive Psychology
- Molecular Biology
- Co-authors
- Junichi YamagishiRoberto Barra-ChicoteJuan Manuel MonteroDaisuke SaitoZhen-Hua LingTomoki TodaTomi KinnunenFernando Villavicencio
- Topics
- Speech Recognition and Synthesis (27 papers)Speech and Audio Processing (15 papers)Natural Language Processing Techniques (9 papers)
- Partner nations
- SpainJapanUnited Kingdom
In The Last Decade
Jaime Lorenzo-Trueba
30 papers receiving 608 citations
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 551
- Signal Processing 366
- Computer Vision and Pattern Recognition 94
- Experimental and Cognitive Psychology 60
- Molecular Biology 23
Countries citing papers authored by Jaime Lorenzo-Trueba
This map shows the geographic impact of Jaime Lorenzo-Trueba'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 Jaime Lorenzo-Trueba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jaime Lorenzo-Trueba more than expected).
Fields of papers citing papers by Jaime Lorenzo-Trueba
This network shows the impact of papers produced by Jaime Lorenzo-Trueba. 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 Jaime Lorenzo-Trueba. The network helps show where Jaime Lorenzo-Trueba may publish in the future.
Co-authorship network of co-authors of Jaime Lorenzo-Trueba
This figure shows the co-authorship network connecting the top 25 collaborators of Jaime Lorenzo-Trueba. A scholar is included among the top collaborators of Jaime Lorenzo-Trueba 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 Jaime Lorenzo-Trueba. Jaime Lorenzo-Trueba is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 8 | |
| 4 | 21 | |
| 5 | 4 | |
| 6 | 7 | |
| 7 | 17 | |
| 8 | 51 | |
| 9 | 184 | |
| 10 | 13 | |
| 11 | 2 | |
| 12 | 39 | |
| 13 | 2 | |
| 14 | 3 | |
| 15 | 10 | |
| 16 | Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers | 111 |
| 17 | Development of a Genre-Dependent TTS System with Cross-Speaker Speaking-Style Transplantation | 1 |
| 18 | 8th ISCA Workshop on Speech Synthesis - Barcelona, Spain | 1 |
| 19 | Towards Speaking Style Transplantation in Speech Synthesis | 11 |
| 20 | 1 |
About Jaime Lorenzo-Trueba
Jaime Lorenzo-Trueba is a scholar working on Signal Processing, Artificial Intelligence and Experimental and Cognitive Psychology, having authored 33 papers that have together received 658 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (27 papers), Speech and Audio Processing (15 papers) and Natural Language Processing Techniques (9 papers). The work is most often cited by research in Signal Processing (366 citations), Artificial Intelligence (551 citations) and Computer Vision and Pattern Recognition (94 citations). Jaime Lorenzo-Trueba has collaborated with scholars based in Spain, Japan and United Kingdom. Frequent co-authors include Junichi Yamagishi, Roberto Barra-Chicote, Juan Manuel Montero, Daisuke Saito, Zhen-Hua Ling, Tomoki Toda, Tomi Kinnunen, Fernando Villavicencio, Ascensión Gallardo-Antolín and Thomas Drugman. Their work appears in journals such as Sensors, Applied Sciences and Speech Communication.
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.