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Early detection of dementia through speech analysis represents a non-invasive and promising approach, but integrating acoustic and linguistic biomarkers remains a technical challenge. In this context, using artificial intelligence models such as Whisper allows for the simultaneous extraction of acoustic representations from voice signals and transcriptions through automatic speech recognition. From those transcriptions, a large language model (LLM) can obtain interpretable features related to lexical diversity, syntactic complexity, and semantic coherence. Combining both modalities through attention-based fusion networks significantly improves diagnostic accuracy, achieving F1 scores above 89%. This advancement demonstrates how artificial intelligence can transform clinical screening.
For companies developing solutions in this field, having robust AI for businesses is essential. Q2BSTUDIO offers custom applications and custom software that integrate machine learning models, AI agents, and data analysis. Additionally, its business intelligence services with Power BI allow for visualizing the results of these models, while cybersecurity capabilities and AWS and Azure cloud services ensure secure and scalable deployment. The combination of these technologies accelerates research and clinical implementation.
Ultimately, the fusion of ASR and LLM not only improves dementia detection but also opens the door to new applications in the healthcare sector. Organizations that adopt AI for businesses and develop custom AI agents are better positioned to lead the next generation of voice-based diagnostics.
