@inproceedings{c6f2698994cc4e10baecfb723161a1bf,
title = "A Deep Learning approach to modeling competitiveness in spoken conversations",
abstract = "The motivation behind the research on overlapping speech has always been dominated by the need to model human-machine interaction for dialog systems and conversation analysis. To have more complex insights of the interlocutors' intentions behind the interaction, we need to understand the type of overlaps. Overlapping speech signals the interlocutor's intention to grab the floor. This act could be a competitive or non-competitive act, which either signals a problem or indicates assistance in communication. In this paper, we present a Deep Learning approach to modeling competitiveness in overlapping speech using acoustic and lexical features and their combination. We compare a fully-connected feed-forward neural network to the Support Vector Machine (SVM) models on real call center human-human conversations. We have observed that feature combination with DNN (significantly) outperforms SVM models, both the individual feature baselines and the feature combination model by 4% and 2% respectively.",
keywords = "Automatic Classification, Context, DNN, Discourse, Overlapping Speech, SVM, Spoken Conversation",
author = "Chowdhury, {Shammur Absar} and Giuseppe Riccardi",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7953244",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5680--5684",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
address = "United States",
}