Age-Aware Adapter Tuning for Children's Speech Recognition

Abstract: Children's automatic speech recognition (ASR) remains challenging because child speech differs from adult speech and varies substantially across developmental stages. While adapter tuning provides a promising way to adapt large pretrained ASR models to children's speech, a single shared child adapter may not fully capture age-dependent variation. In this work, we present one of the first systematic studies of age-aware adapter tuning for child ASR, focusing on speech from children aged 3--12 and older years. We propose age-specialized adapters trained separately for different age groups and compare them with a unified age-conditioned FiLM adapter. With ground-truth age routing, age-specialized adapters improve over the standard shared child adapter baseline from 12.6% to 12.3% overall word error rate (WER) and from 18.4% to 17.6% macro WER, while consistently improving WER for all age groups. We further show that predicted-age routing remains close to ground-truth routing, achieving 12.3% overall WER and 17.8% macro WER without ground-truth age labels at inference. In contrast, unified FiLM conditioning provides smaller gains, indicating that a single unified adapter may be insufficient to capture developmental variation in child speech.