Exploring transfer learning for pathological speech feature prediction:
  Impact of layer selection
Daniela A. Wiepert Rene L. Utianski Joseph R. Duffy John L. Stricker Leland R. Barnard David T. Jones Hugo Botha
There is interest in leveraging AI to conduct automatic, objective
assessments of clinical speech, in turn facilitating diagnosis and treatment of
speech disorders. We explore transfer learning, focusing on the impact of layer
selection, for the downstream task of predicting the presence of pathological
speech. We find that selecting an optimal layer offers large performance
improvements (12.4% average increase in balanced accuracy), though the best
layer varies by predicted feature and does not always generalize well to unseen
data. A learned weighted sum offers comparable performance to the average best
layer in-distribution and has better generalization for out-of-distribution
data.