An integrated system for voice command recognition and emergency detection based on audio signals

• Emergencies and home automation commands are recognised by means of acoustic signals.
• Distress calls are recognised to allow tele-assistance by a relative or a care giver.
• A novelty detection algorithm detects abnormal acoustic events.
• The entire system has been implemented in a low-consuming embedded platform.

The recent reports on population ageing in the most advanced countries are driving governments and the scientific community to focus on technologies for providing assistance to people in their own homes. Particular attention has been devoted to solutions based on acoustic signals since they provide a convenient way to monitor people activities and they enable hands-free human–machine interfaces. In this context, this paper presents a complete solution for voice command recognition and emergency detection based on audio signals entirely integrated in a low-consuming embedded platform. The system combines an active operation mode were distress calls are captured and a vocal interface is enabled for controlling the home automation subsystem, and a pro-active mode, were a novelty detection algorithm detects abnormal acoustic events to alert the user of a possible emergency. In the first operation mode, a Voice Activity Detector captures voice segments of the audio signal, and a speech recogniser detects commands and distress calls. In the pro-active mode, an acoustic novelty detector is employed in order to be able to deal with unknown sounds, thus not requiring an explicit modelling of emergency sounds. In addition, the system integrates a VoIP infrastructure so that emergencies can be communicated to relatives or care centres. The monitoring unit is equipped with multiple microphones and it is connected to the home local area network to communicate with the home automation subsystem. The algorithms have been implemented in a low-consuming embedded platform based on a ARM Cortex-A8 CPU. The effectiveness of the adopted algorithms has been tested on two different databases: ITAAL and A3Novelty. The obtained results show that the adopted solutions are suitable for speech and audio event monitoring in a realistic scenario.