  Despite recent advancements in deep learning technologies, Child Speech
Recognition remains a challenging task. Current Automatic Speech Recognition
(ASR) models require substantial amounts of annotated data for training, which
is scarce. In this work, we explore using the ASR model, wav2vec2, with
different pretraining and finetuning configurations for self-supervised
learning (SSL) toward improving automatic child speech recognition. The
pretrained wav2vec2 models were finetuned using different amounts of child
speech training data, adult speech data, and a combination of both, to discover
the optimum amount of data required to finetune the model for the task of child
ASR. Our trained model achieves the best Word Error Rate (WER) of 7.42 on the
MyST child speech dataset, 2.99 on the PFSTAR dataset and 12.47 on the CMU KIDS
dataset as compared to any other previous methods. Our models outperformed the
wav2vec2 BASE 960 on child speech which is considered a state-of-the-art ASR
model on adult speech by just using 10 hours of child speech data in
finetuning. The analysis of different types of training data and their effect
on inference is also provided by using a combination of datasets in
pretraining, finetuning and inference.
