Acoustic and Linguistic Biomarkers for Cognitive Impairment Detection from Speech

Botelho, Catarina; Gimeno-Gómez, David; Teixeira, Francisco; Mendonça, John; Pereira, Patrícia; Nunes, Diogo A. P.; Rolland, Thomas; Pompili, Anna; Solera-Ureña, Rubén; Ponte, Maria; Matos, David Martins de; Martínez-Hinarejos, Carlos-D.; Trancoso, Isabel; Abad, Alberto

doi:10.21437/Interspeech.2025-91

Acoustic and Linguistic Biomarkers for Cognitive Impairment Detection from Speech

Catarina Botelho, David Gimeno-Gómez, Francisco Teixeira, John Mendonça, Patrícia Pereira, Diogo A. P. Nunes, Thomas Rolland, Anna Pompili, Rubén Solera-Ureña, Maria Ponte, David Martins de Matos, Carlos-D. Martínez-Hinarejos, Isabel Trancoso, Alberto Abad

This work describes a comprehensive approach for the automatic assessment of cognitive decline from spontaneous speech in the context of the PROCESS Challenge 2025. Based on our previous experience on the use of speech and text-derived biomarkers for disease detection, we evaluate here the use of knowledge-based acoustic and text-based feature sets, as well as LLM-based macro-descriptors, and multiple neural representations (e.g., Longformer, ECAPA-TDNN, and Trillsson embeddings). The combination of these feature sets with different classifiers resulted in a large pool of systems, from which, those providing the best balance between train, development, and individual class performance were selected for model ensembling. Our final best-performing systems correspond to combinations of models that are complementary to each other, relying on acoustic and textual information from the three clinical tasks provided in the challenge dataset.