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• W3046013785 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." @default.
• W3046013785 created "2020-08-03" @default.
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• W3046013785 date "2020-07-28" @default.
• W3046013785 modified "2023-10-03" @default.
• W3046013785 title "Detecting and analysing spontaneous oral cancer speech in the wild" @default.
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• W3046013785 hasPublicationYear "2020" @default.
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