Detecting Throat Cancer from Speech Signals Using Machine Learning: A
  Reproducible Literature Review
Mary Paterson James Moor Luisa Cutillo
In this work we perform a scoping review of the current literature on the
detection of throat cancer from speech recordings using machine learning and
artificial intelligence. We find 22 papers within this area and discuss their
methods and results. We split these papers into two groups - nine performing
binary classification, and 13 performing multi-class classification. The papers
present a range of methods with neural networks being most commonly
implemented. Many features are also extracted from the audio before
classification, with the most common bring mel-frequency cepstral coefficients.
None of the papers found in this search have associated code repositories and
as such are not reproducible. Therefore, we create a publicly available code
repository of our own classifiers. We use transfer learning on a multi-class
problem, classifying three pathologies and healthy controls. Using this
technique we achieve an unweighted average recall of 53.54%, sensitivity of
83.14%, and specificity of 64.00%. We compare our classifiers with the results
obtained on the same dataset and find similar results.