The Influence of Automatic Speech Recognition on Linguistic Features and Automatic Alzheimer’s Disease Detection from Spontaneous Speech

Jonathan Heitz, Gerold Schneider, Nicolas Langer

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.
Anthology ID:
2024.lrec-main.1386
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
15955–15969
Language:
URL:
https://aclanthology.org/2024.lrec-main.1386
DOI:
Bibkey:
Cite (ACL):
Jonathan Heitz, Gerold Schneider, and Nicolas Langer. 2024. The Influence of Automatic Speech Recognition on Linguistic Features and Automatic Alzheimer’s Disease Detection from Spontaneous Speech. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15955–15969, Torino, Italia. ELRA and ICCL.
Cite (Informal):
The Influence of Automatic Speech Recognition on Linguistic Features and Automatic Alzheimer’s Disease Detection from Spontaneous Speech (Heitz et al., LREC-COLING 2024)
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