Detecting and analysing spontaneous oral cancer speech in the wild
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ID: 282818
2020
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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.
| Reference Key |
scharenborg2020detecting
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|---|---|
| Authors | Bence Mark Halpern; Rob van Son; Michiel van den Brekel; Odette Scharenborg |
| Journal | arXiv |
| Year | 2020 |
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