1 citations · 1 across the 4 of their papers we have counts for
4 papers
Speech foundation models in healthcare: Effect of layer selection on pathological speech feature prediction
Daniela A. Wiepert, Rene L. Utianski, Joseph R. Duffy +4
Accurately extracting clinical information from speech is critical to the diagnosis and treatment of many neurological conditions. As such, there is interest in leveraging AI for a…
Detecting Speech Abnormalities with a Perceiver-based Sequence Classifier that Leverages a Universal Speech Model
Hagen Soltau, Izhak Shafran, Alex Ottenwess +7
We propose a Perceiver-based sequence classifier to detect abnormalities in speech reflective of several neurological disorders. We combine this classifier with a Universal Speech…
SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality Reduction
Leland Barnard, Farwa Ali, Hugo Botha +1
The most effective dimensionality reduction procedures produce interpretable features from the raw input space while also providing good performance for downstream supervised learn…
Risk of re-identification for shared clinical speech recordings
Daniela A. Wiepert, Bradley A. Malin, Joseph R. Duffy +4
Large, curated datasets are required to leverage speech-based tools in healthcare. These are costly to produce, resulting in increased interest in data sharing. As speech can poten…