Nowadays, it is becoming increasingly affordable to enhance the home environment with several automation schemes, allowing remote control of e.g., heating/cooling, communication, lighting, media, etc. Such smart home functionalities are essential for people with disabilities and the elderly, as they not only provide assistive control of important everyday functionalities, but may prove to be life-saving in case of emergency. However, smart home functionalities may become useless for people who need those most, if they cannot be accessed via a natural, easy to use, interface. The central objective of LISTEN is to design and implement a complete system, including both the software and hardware components, enabling robust hands-free large-vocabulary voice-based access to Internet applications in smart homes. This would allow the users to have natural control (i.e., using their voice) of the smart-home web-enabled functionalities (e.g., turning on/off web-enabled “smart” appliances), but also to access specific Internet applications (e.g., web search, email dictation, access to social networks). A truly hands-free system operation of the voice interface is equally important: users will not have to turn towards a microphone or other device, or wear a headset. Therefore, LISTEN will develop (a) a robust hands-free speech capture system operating as a wireless acoustic sensor network (WASN), specifically designed for the smart home, and (b) a large-vocabulary automatic speech recognition system optimised for accessing web applications and controlling web-enabled smart home automation functionalities. LISTEN pushes the boundaries of current state-of-the-art by bridging the gap between the acoustic front-end and automatic speech recognition research communities, with the common goal of developing a smart-home-specific natural voice interface to web services.

Law Enforcement Agencies (LEAs) across Europe face today important challenges in how they identify, gather and interpret terrorist generated content online. The Dark Web presents additional challenges due to its inaccessibility and the fact that undetected material can contribute to the advancement of terrorist violence and radicalisation. LEAs also face the challenge of extracting and summarising meaningful and relevant content hidden in huge amounts of online data to inform their resource deployment and investigations. In this context, the main objective of the TENSOR project is to provide a powerful terrorism intelligence platform offering LEAs fast and reliable planning and prevention functionalities for the early detection of terrorist organised activities, radicalisation and recruitment. The platform integrates a set of automated and semi-automated tools for efficient and effective searching, crawling, monitoring and gathering online terrorist-generated content from the Surface and the Dark Web; Internet penetration through intelligent dialogue-empowered bots; Information extraction from multimedia (e.g., video, images, audio) and multilingual content; Content categorisation, filtering and analysis; Real-time relevant content summarisation and visualisation; Creation of automated audit trails; Privacy-by-design and data protection. The project brings together industry, LEAs, legal experts and research institutions. It is expected that this collaboration will have significant impact on 1) ensuring the final system meets end-user LEA requirements, 2) enabling LEAs to access and examine terrorist generated content online bringing significant advantages to their operational capability, and 3) promoting industry’s enhanced understanding of operational LEA requirements and their market competitiveness in the field of online organised crime, terrorism and harmful-radicalisation.