mlsanityData to 7 Oct 2026

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

arXiv 2306.03443 ↗First posted 6 Jun 2023cs.CL cs.SD eess.AScited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 6 Jun 2023.

Builds on

12 of 32 references.

References · 12

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 320
  4. 2017Advances in Pre-Training Distributed Word Representationscited by 207
  5. 2020Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challengecited by 104
  6. 2020To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detectioncited by 49
  7. 2020Exploiting Multi-Modal Features From Pre-trained Networks for Alzheimer's Dementia Recognitioncited by 20
  8. 2020Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severitycited by 16
  9. 2021Multi-modal fusion with gating using audio, lexical and disfluency features for Alzheimer's Dementia recognition from spontaneous speechcited by 16
  10. 2020The INESC-ID Multi-Modal System for the ADReSS 2020 Challengecited by 15
  11. 2020Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speechcited by 14
  12. 2021Alzheimers Dementia Detection using Acoustic & Linguistic features and Pre-Trained BERTcited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.

  1. 2024A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunitiescited by 4
  2. 2024Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detectioncited by 2
  3. 2023Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encodercited by 1
  4. 2024Scenario of Use Scheme: Threat Model Specification for Speaker Privacy Protection in the Medical Domaincited by 0
  5. 2025Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0cited by 0