  Automatic Speech Recognition (ASR) for adults' speeches has made significant
progress by employing deep neural network (DNN) models recently, but
improvement in children's speech is still unsatisfactory due to children's
speech's distinct characteristics. DNN models pre-trained on adult data often
struggle in generalizing children's speeches with fine tuning because of the
lack of high-quality aligned children's speeches. When generating datasets,
human annotations are not scalable, and existing forced-alignment tools are not
usable as they make impractical assumptions about the quality of the input
transcriptions. To address these challenges, we propose a new forced-alignment
tool, FASA, as a flexible and automatic speech aligner to extract high-quality
aligned children's speech data from many of the existing noisy children's
speech data. We demonstrate its usage on the CHILDES dataset and show that FASA
can improve data quality by 13.6$\times$ over human annotations.
