In the well-known Turing test, humans have to judge whether they write to another human or a chatbot. In this article, we propose a reversed Turing test adapted to live conversations: based on the speech of the human, we have developed a model that automatically detects whether she/he speaks to an artificial agent or a human. We propose in this work a prediction methodology combining a step of specific features extraction from behaviour and a specific deep learning model based on recurrent neural networks. The prediction results show that our approach, and more particularly the considered features, improves significantly the predictions compared to the traditional approach in the field of automatic speech recognition systems, which is based on spectral features, such as Mel-frequency Cepstral Coefficients (MFCCs). Our approach allows evaluating automatically the type of conversational agent, human or artificial agent, solely based on the speech of the human interlocutor. Most importantly, this model provides a novel and very promising approach to weigh the importance of the behaviour cues used to make correctly recognize the nature of the interlocutor, in other words, what aspects of the human behaviour adapts to the nature of its interlocutor.