  Speech synthesis has come a long way as current text-to-speech (TTS) models
can now generate natural human-sounding speech. However, most of the TTS
research focuses on using adult speech data and there has been very limited
work done on child speech synthesis. This study developed and validated a
training pipeline for fine-tuning state-of-the-art (SOTA) neural TTS models
using child speech datasets. This approach adopts a multi-speaker TTS retuning
workflow to provide a transfer-learning pipeline. A publicly available child
speech dataset was cleaned to provide a smaller subset of approximately 19
hours, which formed the basis of our fine-tuning experiments. Both subjective
and objective evaluations were performed using a pretrained MOSNet for
objective evaluation and a novel subjective framework for mean opinion score
(MOS) evaluations. Subjective evaluations achieved the MOS of 3.95 for speech
intelligibility, 3.89 for voice naturalness, and 3.96 for voice consistency.
Objective evaluation using a pretrained MOSNet showed a strong correlation
between real and synthetic child voices. Speaker similarity was also verified
by calculating the cosine similarity between the embeddings of utterances. An
automatic speech recognition (ASR) model is also used to provide a word error
rate (WER) comparison between the real and synthetic child voices. The final
trained TTS model was able to synthesize child-like speech from reference audio
samples as short as 5 seconds.
