@inproceedings{heitz-etal-2024-influence,
    title = "The Influence of Automatic Speech Recognition on Linguistic Features and Automatic {A}lzheimer{'}s Disease Detection from Spontaneous Speech",
    author = "Heitz, Jonathan  and
      Schneider, Gerold  and
      Langer, Nicolas",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1386",
    pages = "15955--15969",
    abstract = "Alzheimer{'}s disease (AD) represents a major problem for society and a heavy burden for those affected. The study of changes in speech offers a potential means for large-scale AD screening that is non-invasive and inexpensive. Automatic Speech Recognition (ASR) is necessary for a fully automated system. We compare different ASR systems in terms of Word Error Rate (WER) using a publicly available benchmark dataset of speech recordings of AD patients and controls. Furthermore, this study is the first to quantify how popular linguistic features change when replacing manual transcriptions with ASR output. This contributes to the understanding of linguistic features in the context of AD detection. Moreover, we investigate how ASR affects AD classification performance by implementing two popular approaches: A fine-tuned BERT model, and Random Forest on popular linguistic features. Our results show best classification performance when using manual transcripts, but the degradation when using ASR is not dramatic. Performance stays strong, achieving an AUROC of 0.87. Our BERT-based approach is affected more strongly by ASR transcription errors than the simpler and more explainable approach based on linguistic features.",
}

Alzheimer{'}s disease (AD) represents a major problem for society and a heavy burden for those affected. The study of changes in speech offers a potential means for large-scale AD screening that is non-invasive and inexpensive. Automatic Speech Recognition (ASR) is necessary for a fully automated system. We compare different ASR systems in terms of Word Error Rate (WER) using a publicly available benchmark dataset of speech recordings of AD patients and controls. Furthermore, this study is the first to quantify how popular linguistic features change when replacing manual transcriptions with ASR output. This contributes to the understanding of linguistic features in the context of AD detection. Moreover, we investigate how ASR affects AD classification performance by implementing two popular approaches: A fine-tuned BERT model, and Random Forest on popular linguistic features. Our results show best classification performance when using manual transcripts, but the degradation when using ASR is not dramatic. Performance stays strong, achieving an AUROC of 0.87. Our BERT-based approach is affected more strongly by ASR transcription errors than the simpler and more explainable approach based on linguistic features.
Heitz, Jonathan Schneider, Gerold Langer, Nicolas
The Influence of Automatic Speech Recognition on Linguistic Features and Automatic Alzheimer's Disease Detection from Spontaneous Speech