ArticleHealth information science and systems2026
SpeechDETECT: an explainable automated speech processing pipeline for early detection of neurological and health changes.
Article in Health information science and systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Early detection of cognitive impairment remains a critical public health challenge. While biomarkers such as neuroimaging and cerebrospinal fluid analyses offer high sensitivity, their limited accessibility hampers widespread screening, especially in underserved settings. Speech-based markers have emerged as promising, noninvasive indicators of cognitive decline. Objective: To develop and validate SpeechDETECT, an end-to-end speech-processing pipeline that captures fine-grained acoustic and temporal markers of cognitive impairment and provides interpretable outputs suitable for large-scale screening. Methods: SpeechDETECT comprises six modules: (1) noise reduction / amplitude normalization; (2) an eight-domain voice-analysis framework (e.g., frequency parameters, speech fluency); (3) 50 ms segment-level feature extraction; (4) feature visualization; (5) dimensionality reduction / selection (Joint Mutual Information Maximization, LassoNet, PCA); and (6) classifier training with SHapley Additive exPlanations (SHAP). Performance was benchmarked against six acoustic toolkits (e.g., GeMAPS) on two English datasets: the DementiaBank Pitt corpus (train = 166, test = 71) with single cookie-theft picture description task and NIA PREPARE Phase 2 corpus (train = 1 064, test = 267) with multiple speech tasks. Results: A Multi-Layer Perceptron trained on PCA-derived SpeechDETECT features achieved an F1-score = 0.81% and AUC-ROC = 0.80 on the Pitt test set, outperforming the best competing toolkit (AUC = 0.76). On the PREPARE test set-comprising ≤ 30 s recordings from four speech tasks-the same model attained F1 ≈ 0.67% and AUC-ROC = 0.70 Conclusion: SpeechDETECT delivers accurate (AUC up to 0.80) and interpretable detection of early cognitive impairment across both structured and multi-task speech settings. Its fully automated, domain-informed approach enables scalable, speech-based screening and provides a foundation for multimodal systems that combine acoustic markers with clinical or biomarker data to further improve diagnostic precision
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