Listening Between the Lines: Joint Learning of ASR Embeddings and LLM-Augmented Linguistics for Dementia Detection
arXiv:2606.30675v1 Announce Type: cross Abstract: Early detection of dementia through speech analysis offers a non-invasive screening alternative, but capturing both acoustic and linguistic biomarkers remains challenging. We propose a multimodal framework leveraging Whisper for dual-purpose extraction: acoustic representations from encoder outputs and transcripts via automatic speech recognition (ASR). For the acoustic pathway, temporal networks with attention pooling aggregate variable-length sequences into fixed-dimensional embeddings. For the linguistic pathway, we prompt a large language m
Advances in large language models and speech recognition technologies have reached a point where their combined application for complex medical diagnostics is becoming feasible.
This development indicates a growing capability for AI to provide non-invasive, scalable diagnostic tools for significant health challenges like dementia.
The potential for automated, early dementia detection via speech analysis becomes more concrete, shifting from purely research to practical application pathways.
- · Healthcare providers
- · Patients at risk of dementia
- · AI diagnostic companies
- · Speech technology developers
- · Traditional diagnostic methods reliant on manual interpretation
- · Diseases with delayed diagnosis due to current limitations
Improved early detection rates for neurodegenerative diseases through more accessible screening methods.
Reduced healthcare costs associated with late-stage dementia care due to earlier intervention capabilities.
The establishment of speech-based biomarkers as a standard diagnostic tool across various medical fields, leading to new forms of preventative care.
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Read at arXiv cs.LG