Chelsea L Sommer

In this prospective, single-center study, speech samples from 40 children aged 2 to 17 (including individuals with velopharyngeal dysfunction, conditions associated with VPD, and healthy participants) were collected during speech-language pathologist-guided evaluation with consensus CAPS-A-AM ratings, and mel spectrograms of high vowels /i/ and /u/ from sustained vowels, isolated words, and sentences were used to train logistic regression, an EfficientNet-V2-S attention multiple-instance-learning CNN, and a CNN-XGBoost hybrid for binary hypernasality classification at two CAPS-A-AM thresholds (absent 0 versus any hypernasality 1-4, and absent/borderline 0-1 versus mild-to-severe 2-4); multiple independent modeling approaches detected clinically rated hypernasality, with EfficientNet-V2-S a

In this prospective, single-center study, speech samples from 40 children aged 2 to 17 (including individuals with velopharyngeal dysfunction, conditions associated with VPD, and healthy participants) were collected during speech-language pathologist-guided evaluation with consensus CAPS-A-AM ratings, and mel spectrograms of high vowels /i/ and /u/ from sustained vowels, isolated words, and sentences were used to train logistic regression, an EfficientNet-V2-S attention multiple-instance-learning CNN, and a CNN-XGBoost hybrid for binary hypernasality classification at two CAPS-A-AM thresholds (absent 0 versus any hypernasality 1-4, and absent/borderline 0-1 versus mild-to-severe 2-4); multiple independent modeling approaches detected clinically rated hypernasality, with EfficientNet-V2-S a