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Timely detection of cognitive disorders is critical yet often limited by resource-heavy clinical assessments. This paper presents an automated framework for early cognitive screening based on the semantic analysis of spoken image descriptions in Czech. The system integrates automatic speech recognition, formal semantic parsing, and machine learning to evaluate deviations from an expert-defined reference description. The responses of the participants are analyzed for missing or incorrect semantic content, producing structured loss vectors used for classification. Evaluation on a clinically annotated dataset of 268 samples (split into train-test subsets) shows that semantic features outperform traditional lexical and morphological baselines, highlighting the potential of the method for scalable and interpretable cognitive assessment.

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Automatic Cognitive Disorder Detection Through Semantic Analysis of Verbal Image Descriptions

  • 会议论文

  • 发表日期：

    2026年

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  • Tomáš Lebeda,
  • Lucie Zajícová,
  • Jan Švec,
  • Luboš Šmídl

摘要

Timely detection of cognitive disorders is critical yet often limited by resource-heavy clinical assessments. This paper presents an automated framework for early cognitive screening based on the semantic analysis of spoken image descriptions in Czech. The system integrates automatic speech recognition, formal semantic parsing, and machine learning to evaluate deviations from an expert-defined reference description. The responses of the participants are analyzed for missing or incorrect semantic content, producing structured loss vectors used for classification. Evaluation on a clinically annotated dataset of 268 samples (split into train-test subsets) shows that semantic features outperform traditional lexical and morphological baselines, highlighting the potential of the method for scalable and interpretable cognitive assessment.