  Acoustic modeling for child speech is challenging due to the high acoustic
variability caused by physiological differences in the vocal tract. The dearth
of publicly available datasets makes the task more challenging. In this work,
we propose a feature adaptation approach by exploiting adversarial multi-task
training to minimize acoustic mismatch at the senone (tied triphone states)
level between adult and child speech and leverage large amounts of transcribed
adult speech. We validate the proposed method on three tasks: child speech
recognition, child pronunciation assessment, and child fluency score
prediction. Empirical results indicate that our proposed approach consistently
outperforms competitive baselines, achieving 7.7% relative error reduction on
speech recognition and up to 25.2% relative gains on the evaluation tasks.
