This study aims at providing audio-based interaction technology that lets the users have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. The paper presents the sound and speech analysis system evaluated thanks to a corpus of data acquired in a real smart home environment. The 4 steps of analysis are signal detection, speech/sound discrimination, sound classification and speech recognition. The results are presented for each step and globally. The very first experiments show promising results be it for the modules evaluated independently or for the whole system.

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In many human-robot social interactions, where the robot is to interact with only one human throughout the interaction, the "human" side of a conversation is very likely to interact with other humans present in the same room and temporarily loses the focus on the main interaction. These humanhuman interactions can be a very brief chat or a pretty long discussion. To effectively build a human-robot spoken interaction system, one should enable the robot to be aware of the situations where it is (or it is not) the addressee. In many works, gaze tracking and audio localization techniques are used to detect the attention of the subject. In this work we used a combination of voice analysis and head-turning detection to detect if the subject is addressing the robot or if their attention is captured when talking to another person. A subset of the ROMEO2 project corpus is used for experiment. The corpus is made up of 9 hours of social interaction between 27 elderly people and a humanoid robot. This work is done in the context of the ROMEO2 project 1 , whose goal is to develop a humanoid robot that can act as a comprehensive assistant for persons suffering from loss of autonomy.

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