mlsanityData to 19 Oct 2026

Beyond Manual Transcripts: The Potential of Automated Speech Recognition Errors in Improving Alzheimer's Disease Detection

arXiv 2505.19448 ↗First posted 26 May 2025eess.AScited by 0

On the map: Speech and audio language models → ASR evaluation, bias and atypical speech

Abstract (arXiv)

Recent breakthroughs in Automatic Speech Recognition (ASR) have enabled fully automated Alzheimer's Disease (AD) detection using ASR transcripts. Nonetheless, the impact of ASR errors on AD detection remains poorly understood. This paper fills the gap. We conduct a comprehensive study on AD detection using transcripts from various ASR models and their synthesized speech on the ADReSS dataset. Experimental results reveal that certain ASR transcripts (ASR-synthesized speech) outperform manual transcripts (manual-synthesized speech) in detection accuracy, suggesting that ASR errors may provide valuable cues for improving AD detection. Additionally, we propose a cross-attention-based interpretability model that not only identifies these cues but also achieves superior or comparable performance to the baseline. Furthermore, we utilize this model to unveil AD-related patterns within pre-trained embeddings. Our study offers novel insights into the potential of ASR models for AD detection.

Key facts. No citing papers in the map yet (data through 19 Oct 2026). Placed in ASR evaluation, bias and atypical speech, one of 11 directions in Speech and audio language models. Builds on 19 of its 33 references that are in the map. First posted on arXiv 26 May 2025.

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19 of 33 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Leveraging Prompt Learning and Pause Encoding for Alzheimer's Disease Detection9 Dec
  2. 2024Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?10 Jan

Other references · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,923
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,661
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 4,385
  4. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,762
  5. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 2,135
  6. 2024CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Modelscited by 397
  7. 2021Layer-wise Analysis of a Self-supervised Speech Representation Modelcited by 345
  8. 2020Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challengecited by 113
  9. 2021Detecting cognitive decline using speech only: The ADReSSo Challengecited by 89
  10. 2016Attentive Pooling Networkscited by 31
  11. 2023Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challengecited by 19
  12. 2024Clever Hans Effect Found in Automatic Detection of Alzheimer's Disease through Speechcited by 15
5 more
  1. 2024Connected Speech-Based Cognitive Assessment in Chinese and Englishcited by 14
  2. 2023Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detectioncited by 12
  3. 2024Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosiscited by 12
  4. 2022Exploring linguistic feature and model combination for speech recognition based automatic AD detectioncited by 11
  5. 2019Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Otherscited by 9

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  1. 2024Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection
  2. 2026Impact of automatic speech recognition quality on Alzheimer's disease detection from spontaneous speech: a reproducible benchmark study with lexical modeling and statistical validation
  3. 2023Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses
  4. 2021Influence of ASR and Language Model on Alzheimer's Disease Detection
  5. 2021Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech
  6. 2026CoSTA: Cognitive-State-Conditioned TTS Data Augmentation Using ASR Transcripts for Alzheimer's Disease Detection
  7. 2022The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification
  8. 2026Cleaner Speech, Weaker Generalization: Revisiting Pitt-Derived Benchmarks for Alzheimer's Disease Detection