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Alzheimer's Disease (AD) is the world's leading neurodegenerative disease,which often results in communication difficulties. Analysing speech can serveas a diagnostic tool for identifying the condition. The recent ADReSS challengeprovided a dataset for AD classification and highlighted the utility of manualtranscriptions. In this study, we used the new state-of-the-art AutomaticSpeech Recognition (ASR) model Whisper to obtain the transcriptions, which alsoinclude automatic punctuation. The classification models achieved test accuracyscores of 0.854 and 0.833 combining the pretrained FastText word embeddings andrecurrent neural networks on manual and ASR transcripts respectively.Additionally, we explored the influence of including pause information andpunctuation in the transcriptions. We found that punctuation only yielded minorimprovements in some cases, whereas pause encoding aided AD classification forboth manual and ASR transcriptions across all approaches investigated.

Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses