Javier Latorre
- Artificial Intelligence top 2%
- Signal Processing top 2%
- Experimental and Cognitive Psychology top 10%
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Co-authors
- Sabine BuchholzMasami AkamineSadaoki FuruiKoji IwanoMark GalesVincent WanHeiga ZenSacha Krstulović
- Topics
- Speech Recognition and Synthesis (29 papers)Speech and Audio Processing (23 papers)Music and Audio Processing (13 papers)
- Journals
- IEEE Journal of Selected Topics in Signal ProcessingIEEE Transactions on Audio Speech and Language ProcessingSpeech Communication
- Partner nations
- United KingdomJapanSouth Korea
In The Last Decade
Javier Latorre
33 papers receiving 445 citations
Peers
Comparison fields: 5 of 36
- Artificial Intelligence 497
- Signal Processing 377
- Experimental and Cognitive Psychology 71
- Computer Vision and Pattern Recognition 51
- Computer Science Applications 20
Countries citing papers authored by Javier Latorre
This map shows the geographic impact of Javier Latorre'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 Javier Latorre with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Javier Latorre more than expected).
Fields of papers citing papers by Javier Latorre
This network shows the impact of papers produced by Javier Latorre. 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 Javier Latorre. The network helps show where Javier Latorre may publish in the future.
Co-authorship network of co-authors of Javier Latorre
This figure shows the co-authorship network connecting the top 25 collaborators of Javier Latorre. A scholar is included among the top collaborators of Javier Latorre 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 Javier Latorre. Javier Latorre 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 | 4 | |
| 3 | 7 | |
| 4 | 6 | |
| 5 | 6 | |
| 6 | 4 | |
| 7 | An experimental comparison of multiple vocoder types | 30 |
| 8 | Photo-realistic expressive text to talking head synthesis | 10 |
| 9 | Noise Robustness in HMM-TTS Speaker Adaptation | 6 |
| 10 | 1 | |
| 11 | 21 | |
| 12 | 26 | |
| 13 | 69 | |
| 14 | 5 | |
| 15 | HMM-based polyglot speech synthesis by speaker and language adaptive training. | 4 |
| 16 | 1 | |
| 17 | 4 | |
| 18 | 43 | |
| 19 | 5 | |
| 20 | 3 |
About Javier Latorre
Javier Latorre is a scholar working on Signal Processing, Artificial Intelligence and Health Informatics, having authored 35 papers that have together received 554 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (29 papers), Speech and Audio Processing (23 papers) and Music and Audio Processing (13 papers). The work is most often cited by research in Signal Processing (377 citations), Artificial Intelligence (497 citations) and Experimental and Cognitive Psychology (71 citations). Javier Latorre has collaborated with scholars based in United Kingdom, Japan and South Korea. Frequent co-authors include Sabine Buchholz, Masami Akamine, Sadaoki Furui, Koji Iwano, Mark Gales, Vincent Wan, Heiga Zen, Sacha Krstulović, Norbert Braunschweiler and K.M. Knill. Their work appears in journals such as IEEE Journal of Selected Topics in Signal Processing, IEEE Transactions on Audio Speech and Language Processing 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.