  Dementia is a progressive neurological disorder that profoundly affects the
daily lives of older adults, impairing abilities such as verbal communication
and cognitive function. Early diagnosis is essential for enhancing both
lifespan and quality of life for affected individuals. Despite its importance,
diagnosing dementia is complex and often necessitates a multimodal approach
incorporating diverse clinical data types. In this study, we fine-tune Wav2vec
and Word2vec baseline models using two distinct data types: audio recordings
and text transcripts. We experiment with four conditions: original datasets
versus datasets purged of short sentences, each with and without data
augmentation. Our results indicate that synonym-based text data augmentation
generally enhances model performance, underscoring the importance of data
volume for achieving generalizable performance. Additionally, models trained on
text data frequently excel and can further improve the performance of other
modalities when combined. Audio and timestamp data sometimes offer marginal
improvements. We provide a qualitative error analysis of the sentence
archetypes that tend to be misclassified under each condition, providing
insights into the effects of altering data modality and augmentation decisions.
