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Life sciences · Journal article
Health Information Science and Systems · July 24, 2026
Early or partial results. Treat as a signal, not a conclusion.
SpeechDETECT is an automated speech-processing pipeline that extracts acoustic features to classify cognitive impairment in retrospective corpora. The algorithm achieves AUC-ROC of 0.80 on a single-task corpus and 0.70 on a multi-task corpus, outperforming existing acoustic toolkits. This is an early-stage tool development study without prospective validation, clinical outcome data, or demonstration of real-world screening utility.
Retrospective algorithm development and validation study on two established speech corpora with train-test splits. Participants in two English-language speech corpora: DementiaBank Pitt corpus and NIA PREPARE Phase 2 corpus. Presumed to include cognitively impaired and cognitively normal groups, but specific eligibility criteria, demographic details, and diagnostic criteria not provided in extracted text.. Intervention: SpeechDETECT automated speech-processing pipeline with six-module architecture capturing acoustic and temporal markers via voice-analysis framework, feature extraction, and machine-learning classification.. Compared with: Six existing acoustic toolkits including GeMAPS; no comparison to standard clinical cognitive assessments or diagnostic criteria.. Datasets not explicitly located in source text; DementiaBank is a publicly available corpus, NIA PREPARE is NIH-sponsored, implying U.S. origin, but specific sites not stated..
On DementiaBank Pitt test set (n=71): F1-score = 0.81, AUC-ROC = 0.80, outperforming best competing toolkit (AUC = 0.76) On NIA PREPARE Phase 2 test set (n=267, ≤30 s recordings): F1 ≈ 0.67, AUC-ROC = 0.70 Screening top 40% of ranked participants captured ~70% of cognitively-impaired cases in Pitt and ~63% in PREPARE
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SpeechDETECT is presented as a potential screening tool for early cognitive impairment, but this is early-stage algorithm development without prospective clinical validation, diagnostic accuracy in a real screening population, or evidence of clinical decision impact. Clinicians should not adopt this for screening until prospective validation, head-to-head comparison with standard cognitive assessments, and real-world implementation studies are published.
Algorithm development and validation study on two datasets with moderate sample sizes and surrogate endpoints (acoustic features); no clinical outcomes, diagnostic accuracy, or prospective validation reported.
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SpeechDETECT is presented as a potential screening tool for early cognitive impairment, but this is early-stage algorithm development without prospective clinical validation, diagnostic accuracy in a real screening population, or evidence of clinical decision impact. Clinicians should not adopt this for screening until prospective validation, head-to-head comparison with standard cognitive assessments, and real-world implementation studies are published.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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, demonstrating good generalizability. Cumulative-gains analysis showed that screening the top 40% of ranked participants captured ~ 70% of cognitively-impaired (CI) cases in Pitt and ~ 63% in PREPARE. SHAP revealed speech-fluency metrics (hesitation rate, pause ratio) and high-frequency formant dynamics as the most discriminative features.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. The SpeechDETECT toolkit is openly available on GitHub at https://github.com/SpeechCARE/SpeechDETECT-Toolkit for researchers and clinicians. A demo tutorial video showing pipeline usage is available at https://github.com/SpeechCARE/SpeechDETECT-Toolkit/blob/main/SpeechDETECT.mp4.
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