  Deep speaker embeddings have been shown effective for assessing cognitive
impairments aside from their original purpose of speaker verification. However,
the research found that speaker embeddings encode speaker identity and an array
of information, including speaker demographics, such as sex and age, and speech
contents to an extent, which are known confounders in the assessment of
cognitive impairments. In this paper, we hypothesize that content information
separated from speaker identity using a framework for voice conversion is more
effective for assessing cognitive impairments and train simple classifiers for
the comparative analysis on the DementiaBank Pitt Corpus. Our results show that
while content embeddings have an advantage over speaker embeddings for the
defined problem, further experiments show their effectiveness depends on
information encoded in speaker embeddings due to the inherent design of the
architecture used for extracting contents.
