Detecting and analysing spontaneous oral cancer speech in the wild

Autor:	Halpern, Bence Mark, van Son, Rob, Brekel, Michiel van den, Scharenborg, Odette
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to Interspeech 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.14205 Zobrazit plný text záznamu View this record from Arxiv