arXiv:2608.10206v1 Announce Type: new Abstract: 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 Phon
Enables on-device phoneme recognition tuned to children’s speech, supporting privacy-preserving educational/clinical tools with better accuracy for age-specific acoustic patterns.
Published to Cognify News · Week 33, 2026