  Speech synthesis technology has witnessed significant advancements in recent
years, enabling the creation of natural and expressive synthetic speech. One
area of particular interest is the generation of synthetic child speech, which
presents unique challenges due to children's distinct vocal characteristics and
developmental stages. This paper presents a novel approach that leverages the
Fastpitch text-to-speech (TTS) model for generating high-quality synthetic
child speech. This study uses the transfer learning training pipeline. The
approach involved finetuning a multi-speaker TTS model to work with child
speech. We use the cleaned version of the publicly available MyST dataset (55
hours) for our finetuning experiments. We also release a prototype dataset of
synthetic speech samples generated from this research together with model code
to support further research. By using a pretrained MOSNet, we conducted an
objective assessment that showed a significant correlation between real and
synthetic child voices. Additionally, to validate the intelligibility of the
generated speech, we employed an automatic speech recognition (ASR) model to
compare the word error rates (WER) of real and synthetic child voices. The
speaker similarity between the real and generated speech is also measured using
a pretrained speaker encoder.
