Using Digital Speech Markers to Classify Functional Speech Disorder: A Proof‐of‐Concept Pilot Study
Using Digital Speech Markers to Classify Functional Speech Disorder: A Proof‐of‐Concept Pilot Study
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Original abstract
ABSTRACT Background Functional speech disorder (FND‐speech) is a subtype of functional neurological disorder, yet quantitative characterization of its motor and cognitive‐linguistic features remains underexplored. Objective This study aimed to quantitatively characterize FND‐speech by comparing acoustic and linguistic features in individuals with FND‐speech with healthy control subjects (HCs). The potential of digital speech features to serve as adjunctive diagnostic markers was also evaluated. Methods Thirty adults with FND‐speech (females, n = 25; males, n = 5) and 47 age‐, sex‐, education‐, and handedness‐matched HCs (females, n = 29; males, n = 18) were compared using lexicosyntactic, rate‐based, and acoustic markers extracted from a structured picture description task. Supervised machine learning was employed to classify FND‐speech versus HCs. Results Lexicosyntactic features showed moderate predictive power (area under the curve [AUC] = 0.80), as did rate‐based features (AUC = 0.81). Acoustic features demonstrated high discrimination (AUC = 0.98), and a combined model incorporating all feature categories achieved similar performance (AUC = 0.98). Conclusions This proof‐of‐principle pilot study successfully classified FND‐speech versus HCs, highlighting the potential of digital speech markers to serve as adjunctive diagnostic markers for FND‐speech. Out‐of‐sample replication and larger‐scale classifier efforts incorporating neurological controls are needed. © 2025 International Parkinson and Movement Disorder Society.