During an interaction the tendency of speakers to change their speech production to make it more similar to their interlocutor's speech is called convergence. Convergence had been studied due to its relevance for cognitive models of communication as well as for dialogue system adaptation to the user. Convergence effects have been established on controlled data sets while tracking its dynamics on generic corpora has provided positive but more contrasted outcomes. We propose to enrich large conversational corpora with dialogue acts information and to use these acts as filters to create subsets of homogeneous conversational activity. Those subsets allow a more precise comparison between speakers' speech variables. We compare convergence on acoustic variables (Energy, Pitch and Speech Rate) measured on raw data sets, with human and automatically data sets labelled with dialog acts type. We found that such filtering helps in observing convergence suggesting that future studies should consider such high level dialogue activity types and the related NLP techniques as important tools for analyzing conversational interpersonal dynamics.