Speech/voice disorders are common in People Living with Dementia (PLwD). Fluctuations in speech quality can serve as biomarkers of cognitive deterioration but there is a gap in automated assessment of speech collected in unstructured environs. Our organisation has deployed Alexa in the households of 14 PLwD to track self-reported mental and physical state as well as use of language. In this work, we present a case study analysing highly variable speech over time, providing potential insights into cognitive changes. Alexa data gathered from the participant was manually annotated with speech assessment labels. Those labels are matched to openSMILE features by performing a feature importance analysis to isolate critical features that contribute to the perceptual ratings. We can assess phonation with a F1-score of 0.55, breathiness: 0.71, roughness: 0.60, asthenia: 0.65, strain: 0.74. This work is a first step towards automatic speech assessment to monitor cognitive impairment over time.