Search Results for author:
Found 4 papers, 2 papers with code
Exploring transfer learning for pathological speech feature prediction: Impact of layer selection
There is interest in leveraging AI to conduct automatic, objective assessments of clinical speech, in turn facilitating diagnosis and treatment of speech disorders.
Detecting Speech Abnormalities with a Perceiver-based Sequence Classifier that Leverages a Universal Speech Model
We propose a Perceiver-based sequence classifier to detect abnormalities in speech reflective of several neurological disorders.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality Reduction
The most effective dimensionality reduction procedures produce interpretable features from the raw input space while also providing good performance for downstream supervised learning tasks.
Risk of re-identification for shared clinical speech recordings
Risk is high for a small search space but drops as the search space grows ($precision >0. 85$ for $<1*10^{6}$ comparisons, $precision <0. 5$ for $>3*10^{6}$ comparisons).
