Affinity Workshop: Women in Machine Learning

Investigating the Effects of Environmental Factors on the Detection of Laryngeal Cancer from Speech Signals Using Machine Learning.

Mary Paterson · Luisa Cutillo · James Moor


Approximately 2000 people in the UK are diagnosed with laryngeal cancer each year. One of the initial symptoms patients often present with is a change in voice. We propose that an AI system may be able to detect laryngeal cancer patients from non-cancer patients using speech signals. Such a system would be able to classify and prioritise high-risk patients to ensure more appropriate allocation of resources. In order to best implement this type of system into a healthcare setting it would need to be robust to the environmental factors that may affect the speech recordings such as background noise. In early work we have shown that the addition of background noise reduces the precision of classifiers and as such would not relieve the burden on healthcare systems and may, in fact, increase them. In future work we plan to create an AI system that will be robust to environmental factors (such as background noise) such that the system will be usable by patients non specialist recording environments.

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