Detecting and analysing spontaneous oral cancer speech in the wild

Conference paper (2020)

Authors

B.M. Halpern Netherlands Cancer Institute, , Universiteit van Amsterdam

Rob van Son Netherlands Cancer Institute, Universiteit van Amsterdam

Michiel van den Brekel Universiteit van Amsterdam, Netherlands Cancer Institute

O.E. Scharenborg

Explainable AI Pathological speech Corpus Oral cancer speech

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http://resolver.tudelft.nl/uuid:0e44cd3f-bd95-460e-ab22-318ef4e6a8db

Published Date

2020

Language

English

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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.

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