@inproceedings{0e44cd3fbd95460eab22318ef4e6a8db,
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.",
keywords = "Corpus, Explainable AI, Oral cancer speech, Pathological speech",
author = "Halpern, {Bence Mark} and {van Son}, Rob and {van den Brekel}, {Michiel W.M.} and Odette Scharenborg",
year = "2020",
doi = "10.21437/Interspeech.2020-1598",
language = "English",
series = "Interspeech 2020",
publisher = "ISCA",
pages = "4826 -- 4830",
booktitle = "Proceedings of Interspeech 2020",
note = "INTERSPEECH 2020 ; Conference date: 25-10-2020 Through 29-10-2020",
}
