Automated Speech Analysis to Identify Clinical, Anatomical, and Pathological Variants of Primary Progressive Aphasia

Importance: Primary progressive aphasia (PPA) is defined by relatively isolated speech and language symptoms caused by neurodegeneration of language networks; classification of the different clinical, anatomical, and pathological variants relies on time-intensive, expert-dependent assessments that are not widely available. Scalable, interpretable speech-based tools could support diagnosis and monitoring in clinical care and trials. Objective: To determine whether automated speech analysis of voice recording from a short picture description task can yield clinically interpretable speech and language profiles that (1) distinguish among PPA variants, (2) show variant-specific neuroanatomical correlates, and (3) align with underlying autopsy-confirmed neuropathological diagnoses. Design, Setting, and Participants: This was a cross-sectional observational study of patients seen between 2001 and 2025 using the participants' first visit. The setting was a single referral center with external validation in an independent sample from 2 sites. The primary sample included research cohort participants in the following groups: cognitively healthy controls, nonfluent PPA, logopenic PPA, and semantic PPA. Exposures: Picture description task (1-2 minutes of recorded speech) from which 40 linguistic and acoustic features were automatically extracted. Main Outcomes and Measures: The main outcomes included variant-specific speech profile scores derived from Lasso multinomial logistic regression; classification performance for clinical variants and most common underlying neuropathology; and voxelwise associations between speech-profile scores and gray matter volume. Results: A total of 214 participants (mean [SD] age, 65.9 [7.9] years; 118 female [55%]) were included in this analysis (43 in the control group, 50 with nonfluent PPA, 56 with logopenic PPA, and 65 with semantic PPA). Among those with PPA, 64 had postmortem neuropathological data available. Twenty-five features differed between at least 2 PPA variants in 214 patients. Multinomial logistic regression achieved an AUC of 0.90 (95% CI, 0.84-0.97) and generated 3 variant-specific logit scores (speech profiles) using 4 to 8 selected features per variant. External validation in an independent cohort yielded an AUC of 0.90 (95% CI, 0.83-0.97). Profile scores showed associations consistent with established neuroanatomical patterns (n = 195): left superior and middle frontal and premotor cortex in nonfluent PPA, left posterior temporal cortex and angular gyrus in logopenic PPA, and bilateral (left-predominant) anterior temporal lobes in semantic PPA. In an autopsy-confirmed subset with most common underlying pathology (n = 56), speech profile scores discriminated neuropathology with an AUC of 0.90 (95% CI, 0.80-0.96). Conclusions and Relevance: Results of this cross-sectional study suggest that automated speech analysis of a short audio sample of connected speech yielded interpretable speech profiles that accurately distinguished PPA clinical, anatomical, and neuropathological subtypes. These automated speech profiles may serve as clinical tools to support differential diagnosis and longitudinal monitoring, particularly in settings where specialized speech-language assessment is limited.