  Automatic Speech Recognition (ASR) is a critical component of any
fully-automated speech-based dementia detection model. However, despite years
of speech recognition research, little is known about the impact of ASR
accuracy on dementia detection. In this paper, we experiment with controlled
amounts of artificially generated ASR errors and investigate their influence on
dementia detection. We find that deletion errors affect detection performance
the most, due to their impact on the features of syntactic complexity and
discourse representation in speech. We show the trend to be generalisable
across two different datasets for cognitive impairment detection. As a
conclusion, we propose optimising the ASR to reflect a higher penalty for
deletion errors in order to improve dementia detection performance.
