@inproceedings{6cbbf4f5911a46ebac3e0c9984b9ac7e,
title = "Detecting and analysing spontaneous oral cancer speech in the wild",
abstract = "Oral cancer speech is a disease which impacts more than half a million people worldwide every year. Analysis of oral cancer speech has so far focused on read speech. In this paper, we 1) present and 2) analyse a three-hour long spontaneous oral cancer speech dataset collected from YouTube. 3) We set baselines for an oral cancer speech detection task on this dataset. The analysis of these explainable machine learning baselines shows that sibilants and stop consonants are the most important indicators for spontaneous oral cancer speech detection.",
author = "Halpern, {Bence Mark} and {van Son}, Rob and {van den Brekel}, Michiel and Odette Scharenborg",
year = "2020",
doi = "https://doi.org/10.21437/Interspeech.2020-1598",
language = "English",
volume = "2020-October",
series = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
publisher = "International Speech Communication Association",
pages = "4826--4830",
booktitle = "Interspeech 2020",
note = "21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 ; Conference date: 25-10-2020 Through 29-10-2020",
}