Automated Speech Markers of Alzheimer Dementia: Test of Cross-Linguistic Generalizability

• Autores: Pérez Toro, Paula Andrea; Ferrante, Franco Javier; Pérez, Gonzalo Nicolas; Tee, Boon Lead; de Leon, Jessica; Abbona, Cinthia Carolina; Schuster, Maria; Maier, Andreas; Slachevsky, Andrea; Gorno Tempini, Maria Luisa; Ibañez, Agustin Mariano; Orozco Arroyave, Juan Rafael; García, Adolfo Martín
• Año de publicación: 2025
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Alzheimer's disease; Digital biomakers; Automated speech and language analysis
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Background: Automated speech and language analysis (ASLA) is gaining momentum as a noninvasive, affordable, and scalable approach for the early detection of Alzheimer disease (AD). Nevertheless, the literature presents 2 notable limitations. First, many studies use computationally derived features that lack clinical interpretability. Second, a significant proportion of ASLA studies have been conducted exclusively in English speakers. These shortcomings reduce the utility and generalizability of existing findings. Objective: To address these gaps, we investigated whether interpretable linguistic features can reliably identify AD both within and across language boundaries, focusing on English- and Spanish-speaking patients and healthy controls (HCs). Methods: We analyzed speech recordings from 211 participants, encompassing 117 English speakers (58 patients with AD and 59 HCs) and 94 Spanish speakers (47 patients with AD and 47 HCs). Participants completed a validated picture description task from the Boston Diagnostic Aphasia Examination, eliciting natural speech under controlled conditions. Recordings were preprocessed and transcribed before extracting (1) speech timing features (eg, pause duration, speech segment ratios, and voice rate) and (2) lexico-semantic features (lexical category ratios, semantic granularity, and semantic variability). Machine learning classifiers were trained with data from English-speaking patients and HCs, and then tested (1) in a within-language setting (with English-speaking patients and HCs) and (2) in a between-language setting (with Spanish-speaking patients and HCs). Additionally, the features were used to predict cognitive functioning as measured by the Mini-Mental State Examination (MMSE). Results: In the within-language condition, combined speech timing and lexico-semantic features yielded maximal classification (area under the receiver operating characteristic curve [AUC]=0.88), outperforming single-feature models (AUC=0.79 for timing features; AUC=0.80 for lexico-semantic features). Timing features showed the strongest MMSE prediction (R=0.43, P<.001). In the between-language condition, speech timing features generalized well to Spanish speakers (AUC=0.75) and predicted Spanish-speaking patients’ MMSE scores (R=0.39, P<.001). Lexico-semantic features showed lower performance (AUC=0.64) and no significant MMSE prediction (R=–0.31, P=.05). The combined model did not improve results (AUC=0.65; R=0.04, P=.79). Conclusions: These results suggest that while both timing and lexico-semantic features are informative within the same language, only speech timing features demonstrate consistent performance across languages. By focusing on clinically interpretable features, this approach supports the development of clinically usable ASLA tools.
Fil: Pérez Toro, Paula Andrea. Universitat Erlangen Nuremberg; Alemania. Massachusetts General Hospital; Estados Unidos. Universidad de Antioquia; Colombia
Fil: Ferrante, Franco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Pérez, Gonzalo Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina
Fil: Tee, Boon Lead. Trinity College; Irlanda. University of California; Estados Unidos
Fil: de Leon, Jessica. University of California; Estados Unidos
Fil: Abbona, Cinthia Carolina. Universitat Erlangen Nuremberg; Alemania
Fil: Schuster, Maria. Ludwig Maximilians Universitat; Alemania
Fil: Maier, Andreas. Universitat Erlangen Nuremberg; Alemania
Fil: Slachevsky, Andrea. Universidad de Chile; Chile. Hospital del Salvador; Chile. Clínica Alemana; Chile
Fil: Gorno Tempini, Maria Luisa. University of California; Estados Unidos
Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Trinity College; Irlanda. Universidad Adolfo Ibañez; Chile
Fil: Orozco Arroyave, Juan Rafael. Universitat Erlangen Nuremberg; Alemania. Universidad de Antioquia; Colombia
Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Universidad de Santiago de Chile; Chile