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

arXiv: https://arxiv.org/abs/2306.03443 · HTML: https://mlsanity.com/paper/2306.03443/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection](https://mlsanity.com/paper/2008.01551/) (2020)
- [Exploiting Multi-Modal Features From Pre-trained Networks for Alzheimer's Dementia Recognition](https://mlsanity.com/paper/2009.04070/) (2020)
- [Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity](https://mlsanity.com/paper/2009.00700/) (2020)
- [Multi-modal fusion with gating using audio, lexical and disfluency features for Alzheimer's Dementia recognition from spontaneous speech](https://mlsanity.com/paper/2106.09668/) (2021)
- [The INESC-ID Multi-Modal System for the ADReSS 2020 Challenge](https://mlsanity.com/paper/2005.14646/) (2020)
- [Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speech](https://mlsanity.com/paper/2006.07358/) (2020)
- [Alzheimers Dementia Detection using Acoustic & Linguistic features and Pre-Trained BERT](https://mlsanity.com/paper/2109.11010/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection](https://mlsanity.com/paper/2409.12541/) (2024)
- [Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder](https://mlsanity.com/paper/2310.03985/) (2023)
- [Scenario of Use Scheme: Threat Model Specification for Speaker Privacy Protection in the Medical Domain](https://mlsanity.com/paper/2409.16106/) (2024)
- [Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0](https://mlsanity.com/paper/2501.16201/) (2025)

## Abstract (arXiv)

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.