  Many recently published Text-to-Speech (TTS) systems produce audio close to
real speech. However, TTS evaluation needs to be revisited to make sense of the
results obtained with the new architectures, approaches and datasets. We
propose evaluating the quality of synthetic speech as a combination of multiple
factors such as prosody, speaker identity, and intelligibility. Our approach
assesses how well synthetic speech mirrors real speech by obtaining correlates
of each factor and measuring their distance from both real speech datasets and
noise datasets. We benchmark 35 TTS systems developed between 2008 and 2024 and
show that our score computed as an unweighted average of factors strongly
correlates with the human evaluations from each time period.
