- Assessing spoken language abilities in children is critical for early detection of developmental delays. We propose a two-stage framework for automatic pronunciation assessment, targeting classification of items in a language development screening. In the first stage, we fine-tune a sentence transformer using phonetic and label-informed losses to create a structured latent text space. In the second stage, we fine-tune an audio encoder to project spoken utterances into the text space. Our approach enables inference from audio alone while preserving alignment with phonetically meaningful structure. Compared to baseline systems, our method achieves the best classification performance and provides a latent space suitable for downstream analysis. This facilitates the identification of phonological or grammatical error patterns from cluster structure, paving the way for potentially interpretable assessment
of early language acquisition.
