Jean-François Sérignat
- Artificial Intelligence
- Signal Processing top 10%
- Computer Vision and Pattern Recognition
- Pulmonary and Respiratory Medicine
- Biomedical Engineering
- Co-authors
- Eric CastelliMichel VacherDan IstrateLaurent BesacierPedro MayorgaDominique VaufreydazLe Xuan HungAbdelghani Harrag
- Topics
- Speech and Audio Processing (6 papers)Speech Recognition and Synthesis (5 papers)Advanced Data Compression Techniques (4 papers)
- Journals
- Language Resources and EvaluationPhoneticaIEEE Transactions on Information Technology in Biomedicine
- Partner nations
- United StatesFranceAlgeria
In The Last Decade
Jean-François Sérignat
10 papers receiving 143 citations
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 78
- Signal Processing 74
- Computer Vision and Pattern Recognition 49
- Pulmonary and Respiratory Medicine 23
- Biomedical Engineering 19
Countries citing papers authored by Jean-François Sérignat
This map shows the geographic impact of Jean-François Sérignat'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 Jean-François Sérignat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jean-François Sérignat more than expected).
Fields of papers citing papers by Jean-François Sérignat
This network shows the impact of papers produced by Jean-François Sérignat. 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 Jean-François Sérignat. The network helps show where Jean-François Sérignat may publish in the future.
Co-authorship network of co-authors of Jean-François Sérignat
This figure shows the co-authorship network connecting the top 25 collaborators of Jean-François Sérignat. A scholar is included among the top collaborators of Jean-François Sérignat 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 Jean-François Sérignat. Jean-François Sérignat is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 72 | |
| 3 | 1 | |
| 4 | 9 | |
| 5 | 15 | |
| 6 | 36 | |
| 7 | Elaboración de un corpus balanceado para el cálculo de modelos acústicos usando la web | 1 |
| 8 | A NEW METHODOLOGY FOR SPEECH CORPORA DEFINITION FROM INTERNET DOCUMENTS | 10 |
| 9 | EMACOP : Environnement Multimédia pour l'Acquisition et la gestion de COrpus Parole | 2 |
| 10 | 1 |
About Jean-François Sérignat
Jean-François Sérignat is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology, having authored 10 papers that have together received 157 indexed citations. Recurring topics across this work include Speech and Audio Processing (6 papers), Speech Recognition and Synthesis (5 papers) and Advanced Data Compression Techniques (4 papers). The work is most often cited by research in Signal Processing (74 citations), Artificial Intelligence (78 citations) and Computer Vision and Pattern Recognition (49 citations). Jean-François Sérignat has collaborated with scholars based in United States, France and Algeria. Frequent co-authors include Eric Castelli, Michel Vacher, Dan Istrate, Laurent Besacier, Pedro Mayorga, Laurent Besacier, Dominique Vaufreydaz, Le Xuan Hung, Abdelghani Harrag and Norbert Noury. Their work appears in journals such as Language Resources and Evaluation, Phonetica and IEEE Transactions on Information Technology in Biomedicine.
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.