ROXANNE Consortium Meeting

HENSOLDT Analytics Participates in ROXANNE’s Second Consortium Meeting


ROXANNE's Second Consortium Meeting

ROXANNE Project‘s second consortium meeting took place during November 2-4, 2021. After several months of online meetings, the members of the project, including HENSOLDT Analytics, met in a hybrid format.

The research team from HENSOLDT Analytics consisted of Gerhard Backfried, Erinc Dikici, Miroslav Janosik, Katja Prinz and Dorothea Thomas-Aniola. The physical part of the meeting took place on November 2nd, 2021 in Munich at the Hyperion Hotel.

On the 3rd and 4th of November HENSOLDT Analytics, ZITiS and Phonexia organized a joint data-collection workshop. 30 participants joined forces for the ROXANNE Simulated Dataset. Project partners from 17 countries were present, including representatives from the academic, industrial partners and LEAs and a further 26 participants joined online.

The partners presented the status of their current work as well as their plans for the last year of the project. In a special session during the event, a demonstration of the “Autocrime” took place.  

Work on the ROXANNE project will continue throughout the next year, extending the current suite of technologies as well as the ROXSD dataset.

Investigating Criminal Networks with New Technologies

The ROXANNE project (real time network, text and speaker analytics for combating organized crime) is a research project funded by the European Union and it aims to develop an analytics platform enhancing investigation capabilities to unmask criminal networks.

HENSOLDT Analytics is leading the data management work package and is also contributing with its speech and language processing technologies in ROXANNE.

Read about the latest field test of the project.

Find out more about the ROXANNE Project here.

HENSOLDT Analytics
HENSOLDT Analytics

HENSOLDT Analytics is a global leading provider of Open Source Intelligence (OSINT) systems and Natural Language Processing technologies, such as Automatic Speech Recognition, which are key elements for media monitoring and analysis.