The lack of large-scale speech corpora for Cantonese and older adults has impeded the academia's research of automatic speech recognition (ASR) systems for the two. On the other hand, the recent success of self-supervised speech representation learning has shown its competitiveness in low-resource ASR. This work therefore studies the application of wav2vec 2.0 ASR using monolingual and cross-lingual pre-trained models on a developing speech corpus, CU-MARVEL, which is dedicated to the automated screening of neurocognitive disorders (NCD) for Cantonese-speaking older adults in Hong Kong. We detail our data preparation procedures for creating a monolingual wav2vec 2.0 model from scratch and further pre-training a cross-lingual model. We report the performance of our wav2vec 2.0 ASR models on the said corpus and present a preliminary analysis of the relationship between the ASR performance of older adult speech and various demographic characteristics.