Segment-Level Mandarin Chinese Speech-Based Cognitive Impairment Detection via an Autoencoder with Contrastive Learning
arXiv:2606.19996v1 Announce Type: cross Abstract: \noindent\textbf{Background and Objective:} Speech has emerged as a low-cost and non-invasive digital biomarker with considerable potential for cognitive impairment detection. However, limited labeled data and cross-dataset variability remain major challenges for robust speech-based screening systems. \par\noindent\textbf{Methods:} We developed a segment-level representation learning framework for speech-based cognitive impairment detection. Speech recordings were divided into short segments and converted into spectrogram representations. To im
Advances in AI, particularly in speech processing and contrastive learning, are enabling more sophisticated and less invasive diagnostic methods for cognitive impairments.
This development allows for earlier, more accessible, and lower-cost detection of cognitive impairment using non-invasive digital biomarkers, potentially transforming geriatric healthcare and preventative medicine.
The ability to accurately detect cognitive impairment from speech segments, even with limited labeled data, reduces reliance on traditional, often costly and invasive, diagnostic processes.
- · Healthcare providers
- · Aging populations
- · AI diagnostic companies
- · Geriatric care services
- · Traditional cognitive assessment providers
- · Late-stage disease management (if early detection becomes widespread)
Widespread adoption of speech-based diagnostics for cognitive health.
Increased demand for AI models capable of processing diverse linguistic and acoustic data for medical applications.
Proactive public health interventions for cognitive decline become feasible, potentially extending healthy lifespans and reducing healthcare burdens.
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