Mixture-of-Experts for Age-Inclusive ASR: Reducing the Adult-Child Performance Gap in Multilingual Speech Recognition

Abstract

Automatic speech recognition (ASR) remains less accurate for children than for adults. This difference is poorly understood in multilingual systems. This thesis examines whether adding sparse Mixture-of-Experts (MoE) layers to a shared FastConformer-CTC encoder can narrow the adult-child word error rate (WER) gap. It also tests whether experts specialise by age, even without age labels in the training data. Dense and MoE encoders were initialised from the same pretrained checkpoint and fine-tuned in two stages: first on adult speech, then on mixed adult-child speech. Experiments covered English, Dutch, German, and Polish. Paired speaker-level bootstrap tests compared WER gaps. In the English-only experiment, MoE reduced the gap from 22.33 to 19.97 percentage points after adult-only fine-tuning and from 11.05 to 10.69-10.78 points after child-speech adaptation. It also lowered adult and child WER. Part of this advantage emerged before any child speech audio was used, suggesting an architectural contribution beyond data exposure. In the multilingual experiment, the benefit did not generalise uniformly. MoE reduced the gap significantly only for English. Dutch showed a smaller, non-significant reduction. German showed an increase in child WER and a significant widening of the adult-child gap, despite improved adult WER. The Polish results were difficult to interpret, as the corpus contained only isolated words and children with sigmatism. A routing analysis of the English models found age-correlated differences in early and late encoder regions after child-speech fine-tuning. The experts that adult and child speech were routed to produced distinct representations. An English domain-control analysis did not reproduce this pattern. Language was the dominant routing factor, and age-related specialisation was layered on top. Overall, a single shared MoE encoder can reduce the adult-child gap and develop age-related specialisation without demographic labels at inference time. Its effect depends on the coverage and domain match of the child speech data in each language. This makes the sparse MoE encoder a practical direction for more inclusive speech recognition: one model that reduces the adult-child gap without age labels, and whose routing can be examined to explain the improvement.