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Updated: Jan 10, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Domain generalization for voice-based cognitive impairment detection.

Minsoo Kim1, Young Chul Youn2,3, Yugwon Won1

  • 1Research and Development, Baikal AI Inc., Seoul, Republic of Korea.

BMC Medical Informatics and Decision Making
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a robust AI model for early cognitive disorder detection using voice biomarkers. Domain generalization improved diagnostic accuracy across diverse recording conditions, enhancing reliability for AI-powered healthcare.

Keywords:
Alzheimer’s diseaseBiomarkerDementiaDomain generalizationMachine learningVoice

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Area of Science:

  • Artificial Intelligence
  • Biomedical Engineering
  • Computational Linguistics

Background:

  • Voice biomarkers show promise for early detection of cognitive disorders.
  • Variations in recording environments challenge AI model accuracy for cognitive impairment diagnosis.
  • Developing generalizable AI models is crucial for reliable detection across diverse datasets.

Purpose of the Study:

  • To develop a robust and generalizable AI model for diagnosing cognitive impairments.
  • To overcome challenges posed by varied recording conditions in voice biomarker data.
  • To enhance the reliability of AI-driven cognitive disorder detection.

Main Methods:

  • Implemented a domain generalization approach using an adapted Deep Domain-Adversarial Image Generation (DDAIG) framework.
  • Transformed input data to minimize center-specific characteristics and emphasize domain-invariant features.
  • Focused on features indicative of cognitive impairment for improved model generalization.

Main Results:

  • Cognitive impairment (CI) classification accuracy was 0.90 after domain generalization.
  • Center classification accuracy dropped from 0.96 to 0.64, indicating reduced dependence on site-specific data.
  • The reduction in center classification metrics demonstrates effective domain generalization.

Conclusions:

  • The adapted DDAIG framework successfully reduced center-specific learning in AI models.
  • Enhanced generalization of cognitive impairment classification across different data centers was achieved.
  • Domain generalization is vital for creating reliable AI diagnostic tools for cognitive disorders.