  Linguistic anomalies detectable in spontaneous speech have shown promise for
various clinical applications including screening for dementia and other forms
of cognitive impairment. The feasibility of deploying automated tools that can
classify language samples obtained from speech in large-scale clinical settings
depends on the ability to capture and automatically transcribe the speech for
subsequent analysis. However, the impressive performance of self-supervised
learning (SSL) automatic speech recognition (ASR) models with curated speech
data is not apparent with challenging speech samples from clinical settings.
One of the key questions for successfully applying ASR models for clinical
applications is whether imperfect transcripts they generate provide sufficient
information for downstream tasks to operate at an acceptable level of accuracy.
In this study, we examine the relationship between the errors produced by
several deep learning ASR systems and their impact on the downstream task of
dementia classification. One of our key findings is that, paradoxically, ASR
systems with relatively high error rates can produce transcripts that result in
better downstream classification accuracy than classification based on verbatim
transcripts.
