Abstract EN:

During a conversation, participants tend to tune, consciously or not, their communicative production in regards to their interlocutor. It is generally admitted, that under standard circumstances, these phenomena result in convergence of the two participants’ speech parameters. Past literature offers a large part of studies describing the effects of convergence in interpersonal dynamics but there are still some unclear aspects. These concerns firstly the mechanisms that rule the phenomenon in natural conversations. These are hard to be studied due to the spontaneous flow of the conversants that results to be noisy and variable. In second place in this kind of conversation is still not well known how participants modify their speech style (the dynamics i.e.) in the course of the conversation. In this thesis, we aim to validate previous results in acoustic-prosodic convergence and provide novel approaches to have a partial a posteriori filter on natural conversations and to track the interpersonal dynamics. We used classical machine learning approaches (Linear mixed models, Random forest e.g.) and more recent algorithms of deep learning (LSTM architecture). These results extend the landscape of convergence effects in the not controlled dataset and offer novel approaches, concerning the method to control the variability of natural conversations and the prediction task paradigm to evaluate the interpersonal dynamics, consisting in evaluating the influence of the speaker and interlocutor on each other speech style.