Structured phonological representations provide an interpretable alternative to generic speech embeddings, but existing models are largely trained on adult speech.
We adapt PhonoQ-2.0 to child speech using CHILDES-Aligned data and compare three alignment-supervision conditions (Adult, Adult+Child, and Child-only) across two initialization strategies (Adult PhonoQ and scratch).
Generalization is evaluated against manual child-speech annotations.
On 1,352 consonant targets from 58 typically developing children, child-speech adaptation improves voicing recognition across all supervision conditions, from 0.922 macro-F1 for Adult PhonoQ to 0.972--0.987 after adaptation.
Manner is more sensitive to alignment supervision: Adult+Child MFA reaches 0.804 and 0.796, compared to approximately 0.70 under Adult MFA supervision.
Place remains comparatively strong across systems (0.871--0.902), although per-class performance varies substantially.
The velar-fronting contrast is preserved across all seven model variants.
Longitudinal UltraPhonix analysis further reveals speaker-specific velar and post-alveolar changes that are largely preserved across models and broadly consistent with reported clinical progress.