Deep Learning Techniques for Phoneme Recognition in Italian Children' s Speech

Abstract:Speech therapists often face difficulties diagnosing impairments due to the lack of efficient tools for transcribing speech into the International Phonetic Alphabet (IPA). This work addresses this challenge with Broca, a Conformer-based deep learning system pretrained on 8 days of adult speech and fine-tuned on a 165-minute dataset of Italian child speech collected through a range of standardized diagnostic tests for children aged 3.5-6.5. Broca was optimized to handle phonetic variability in children's speech, including tone, accent, and speech errors, and achieved a state-of-the-art weighted Phoneme Error Rate of 13.36% on Italian speech. Remarkably, this performance was obtained using less than three hours of child-specific data, underscoring the model's efficiency and robustness in low-resource clinical settings. This work demonstrates that accurate, vocabulary-independent speech-to-IPA transcription can be achieved with minimal data, paving the way for more accessible, data-efficient tools to support speech assessment and diagnosis.

Machine Learning (cs.LG)

arXiv:2609.33060 [cs.LG]

(or arXiv:2609.33060v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.33060 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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