Task-Aligned Speech Representations for Intelligibility Classification in Patients with Cleft Palate

Perceptual evaluation remains the clinical standard for postoperative cleft-palate speech, but objective patient-level assessment of overall speech intelligibility remains limited. We developed a patient-level three-class classification model using frozen Chinese HuBERT representations, principal component analysis (PCA), and regularized multinomial logistic regression. The study included 123 patients with cleft palate, including 100 in the development cohort and 23 in the held-out cohort. Each patient completed 27 standardized articulation tasks. HuBERT embeddings from each task were summarized by temporal mean and standard deviation and concatenated in a fixed clinical order to form a patient-level representation. Model development used patient-disjoint nested cross-validation followed by locked held-out evaluation. The primary model achieved 90.00% accuracy and a macro-F1 of 0.8659 in development and 86.96% accuracy, a macro-F1 of 0.8227, 93.75% balanced accuracy, and a quadratic-weighted kappa of 0.9043 in the held-out cohort. All three held-out errors involved patients with Poor intelligibility who were misclassified as Reduced intelligibility, with no direct Normal-to-Poor errors. Comparisons across six classifiers and four pretrained encoders showed no significant paired differences after Holm correction. Task-replacement analysis revealed heterogeneous reliance on individual articulation tasks. These findings support the feasibility of objective patient-level speech intelligibility assessment from standardized cleft-palate speech recordings.