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.

本研究使用最新的自动语音识别技术，研究如何应用暂停符号和自动标点符号进行阿尔茨海默病分类，实验结果表明，这两种技术与手工转录结合可有效识别阿尔茨海默病。