aiqbench analysis
This study advances speech processing by adapting the PhonoQ-2.0 model to interpret child phonological development, addressing a critical gap where existing systems are trained primarily on adult data. The research demonstrates that aligning models with CHILDES-aligned datasets significantly improves recognition accuracy for voicing and manner distinctions in young speakers while preserving clinical progress markers. This work is essential for building robust AI tools capable of analyzing early language acquisition and supporting pediatric speech therapy applications.
Child-Adapted Structured Phonological Representations for Interpretable Speech Sound Analysis
arXiv:2610.00852v1 Announce Type: new Abstract: 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.
Abstract: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.
Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
arXiv:2610.00852 [cs.CL]
(or arXiv:2610.00852v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.00852 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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