A Deep Learning approach to modeling competitiveness in spoken conversations
Overlapping speech is one of the most frequently occurring events in the course of human-human conversations. Understanding the dynamics of overlapping speech is crucial for conversational analysis and for modeling human-machine dialog. Overlapping speech may signal the speaker’s intention to grab the floor with a competitive vs non-competitive act. In this paper, we study the role of speakers, whether they initiate (overlapper) or not (overlappee) the overlap, and the context of the event. The speech overlap may be explained and predicted by the dialog context, the linguistic or acoustic descriptors. Our goal is to understand whether the competitiveness of the overlap is best predicted by the overlapper, the overlappee, the context or by their combinations. For each overlap and its context we have extracted acoustic, linguistic, and psycholinguistic features and combined decisions from the best classification models. The evaluation of the classifier has been carried out over call center human-human conversations. The results show that the complete knowledge of speakers’ role and context highly contribute to the classification results when using acoustic and psycholinguistic features. Our findings also suggest that the lexical selections of the overlapper are good indicators of speaker’s competitive or non-competitive intentions.