Alzheimer Disease Classification through ASR-based Transcriptions:
  Exploring the Impact of Punctuation and Pauses
Lucía Gómez-Zaragozá Simone Wills Cristian Tejedor-Garcia Javier Marín-Morales Mariano Alcañiz Helmer Strik
Alzheimer's Disease (AD) is the world's leading neurodegenerative disease,
which often results in communication difficulties. Analysing speech can serve
as a diagnostic tool for identifying the condition. The recent ADReSS challenge
provided a dataset for AD classification and highlighted the utility of manual
transcriptions. In this study, we used the new state-of-the-art Automatic
Speech Recognition (ASR) model Whisper to obtain the transcriptions, which also
include automatic punctuation. The classification models achieved test accuracy
scores of 0.854 and 0.833 combining the pretrained FastText word embeddings and
recurrent neural networks on manual and ASR transcripts respectively.
Additionally, we explored the influence of including pause information and
punctuation in the transcriptions. We found that punctuation only yielded minor
improvements in some cases, whereas pause encoding aided AD classification for
both manual and ASR transcriptions across all approaches investigated.