Detecting and Analysing Spontaneous Oral Cancer Speech in the Wild

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Conference object 25 Oct 2020Embargo end date: 01 Jan 2020 Netherlands Publisher:ISCAJournal:Interspeech 2020Funded by:EC | TAPAS

Authors: Bence Mark Halpern; Rob van Son; Michiel van den Brekel; Odette Scharenborg;

doi: 10.21437/interspeech.2020-1598 , 10.48550/arxiv.2007.14205

arXiv: 2007.14205

handle: 11245.1/08afe3cb-c359-440c-907d-10ec2ff38f1e

Detecting and Analysing Spontaneous Oral Cancer Speech in the Wild

- Summary
- Subjects
- Metrics

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.

Accepted to Interspeech 2020

Country

Netherlands

Related Organizations

Amsterdam UMC
Netherlands
Delft University of Technology
Netherlands
Antoni van Leeuwenhoek Hospital
Netherlands
Netherlands Heart Institute
Netherlands
TECHNISCHE UNIVERSITEIT DELFT
Netherlands

View all View all

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), 610, Computer Science - Sound, Machine Learning (cs.LG), Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Audio and Speech Processing

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	6
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average