Work

Detecting and Analysing Spontaneous Oral Cancer Speech in the Wild

Year: 2020

Type: conference-paper

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.

Source: Interspeech (USB)

DOI: 10.21437/interspeech.2020-1598

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

Institutions: The Netherlands Cancer Institute, University of Amsterdam, Delft University of Technology

Language: English

Topic: Voice and Speech Disorders

Subfield: Speech and Hearing

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: European Commission