@inproceedings{a63c2098714145868059bc03199c3c66,
title = "A data-driven approach to detection of interruptions in human-human conversations",
abstract = "We report the results of our initial ef- forts towards automatic detection of user{\textquoteright}s interruptions in a spoken human– machine dialogue. In a first step, we explored the use of automatically ex- tractable acoustic features, frequency and intensity, in discriminating listen- er{\textquoteright}s interruptions in human–human conversations. A preliminary analysis of interaction snippets from the HCRC Map Task corpus suggests that for the task at hand, intensity is a stronger fea- ture than frequency, and using intensity in combination with feature loudness offers the best results for a k-means clustering algorithm",
author = "Raveesh Meena and Saeed Dabbaghchian and Kalin Stefanov",
year = "2014",
language = "English",
isbn = "9789163756627",
series = "Proceedings from FONETIK 2014 Stockholm, June 9-11, 2014 PERILUS XXIV, June 2014",
publisher = "Stockholm University",
pages = "29--32",
editor = "Heldner, {Mattias }",
booktitle = "Proceedings from FONETIK 2014 Stockholm, June 9-11, 2014",
}