Detecting and analysing spontaneous oral cancer speech in the wild
This work addresses oral cancer speech analysis for medical applications, but it is incremental as it extends existing methods to new data.
The paper tackled the problem of analyzing spontaneous oral cancer speech by presenting and analyzing a three-hour dataset from YouTube, setting baselines for detection and finding that sibilants and stop consonants are key indicators.
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.