Edge Phoneme Recognition for Children's Speech through Age-Aware Training
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Abstract (arXiv)
Detecting phonemes from children's speech has historically been difficult due to the scarcity of training data, and unique characteristics of children's speech. During a phoneme detection competition, we found that training a lightweight model to predict the age of the learner, as well as the phoneme sequence, enabled a 94M-parameter model to outperform WavLM Large models (317M) on the target DrivenData distribution, and fall within approximately 0.04 CER of competition ensembles with 90 times the parameters. This has enabled the creation of PhonemeTrainer, an application that can run on most modern cellular phones. This will ultimately enable better Automated Speech Recognition (ASR) and pronunciation helper apps for children's speech, with the privacy and compliance benefits that come with edge processing.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 10 Aug 2026.
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References · 5
- 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
- 2024Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizationscited by 7
- 2016Harnessing the Power of the Crowd to Increase Capacity for Data Science in the Social Sectorcited by 6
- 2025Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learningcited by 6
- 2026Mind the Shift: Using Delta SSL Embeddings to Enhance Child ASRcited by 3
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