← 研踪 PaperTrailarXiv:2611.16968 · v1
Morphological Simplification in Alzheimer's Disease: Computational Analysis and Identification From Connected Speech
首次提交 2026-11-12 23:36
版本更新 2026-11-12 23:36
雷达观测 2026-11-13 14:15
摘要
Purpose: Morphology is often regarded as relatively spared in Alzheimer's disease (AD) and is rarely modeled in connected speech. This study asked which morphological features distinguish speakers with AD from healthy controls, how well morphology classifies AD compared with other linguistic levels and BERT-based models, and whether the signal exceeds transcript length and vocabulary. Method: Fifty-five automatic measures in eight families, seven of them new and theory-motivated, were computed from participant speech in the ADReSS Cookie Theft descriptions (78 AD, 78 controls) and analyzed with group comparisons, equivalence tests, SHAP, family ablation, likelihood-ratio tests, feature-selection classifiers, and BERT-based models. The automatic measures were validated manually, and models were transferred to two other corpora. Results: Nineteen of 38 measures differed between groups: speakers with AD produced fewer suffixed and inflected words, shorter verbs and nouns, and fewer present-progressive but more past-tense forms (largest effect r = -0.42), whereas errors were rare and statistically equivalent between groups. Eight measures survived adjustment for length and lexical frequency. With test-set feature selection, morphology reached a macro-F1 of 0.85, on par with syntax and above fine-tuned BERT (0.80 over five seeds); fully cross-validated selection gave 0.62. Adding morphology to BERT improved AUC (0.91 vs. 0.89) but not macro-F1. Transfer was limited, although two thirds of the effects kept their direction in mild cognitive impairment. Conclusions: AD connected speech shows morphological simplification and temporal reanchoring rather than breakdown. Morphology is more informative than its reputation suggests. Keywords: Alzheimer's disease; morphology; connected speech; ADReSS; inflection; tense and aspect; SHAP; machine learning; BERT
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v1 · 2026-11-12 23:36 · 最新观测版本
Morphological Simplification in Alzheimer's Disease: Computational Analysis and Identification From Connected Speech
Purpose: Morphology is often regarded as relatively spared in Alzheimer's disease (AD) and is rarely modeled in connected speech. This study asked which morphological features distinguish speakers with AD from healthy controls, how well morphology classifies AD compared with other linguistic levels and BERT-based models, and whether the signal exceeds transcript length and vocabulary. Method: Fifty-five automatic measures in eight families, seven of them new and theory-motivated, were computed from participant speech in the ADReSS Cookie Theft descriptions (78 AD, 78 controls) and analyzed with group comparisons, equivalence tests, SHAP, family ablation, likelihood-ratio tests, feature-selection classifiers, and BERT-based models. The automatic measures were validated manually, and models were transferred to two other corpora. Results: Nineteen of 38 measures differed between groups: speakers with AD produced fewer suffixed and inflected words, shorter verbs and nouns, and fewer present-progressive but more past-tense forms (largest effect r = -0.42), whereas errors were rare and statistically equivalent between groups. Eight measures survived adjustment for length and lexical frequency. With test-set feature selection, morphology reached a macro-F1 of 0.85, on par with syntax and above fine-tuned BERT (0.80 over five seeds); fully cross-validated selection gave 0.62. Adding morphology to BERT improved AUC (0.91 vs. 0.89) but not macro-F1. Transfer was limited, although two thirds of the effects kept their direction in mild cognitive impairment. Conclusions: AD connected speech shows morphological simplification and temporal reanchoring rather than breakdown. Morphology is more informative than its reputation suggests. Keywords: Alzheimer's disease; morphology; connected speech; ADReSS; inflection; tense and aspect; SHAP; machine learning; BERT
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