  Text-to-speech is now able to achieve near-human naturalness and research
focus has shifted to increasing expressivity. One popular method is to transfer
the prosody from a reference speech sample. There have been considerable
advances in using prosody transfer to generate more expressive speech, but the
field lacks a clear definition of what successful prosody transfer means and a
method for measuring it.
  We introduce a dataset of prosodically-varied reference natural speech
samples for evaluating prosody transfer. The samples include global variations
reflecting emotion and interpersonal attitude, and local variations reflecting
topical emphasis, propositional attitude, syntactic phrasing and marked
tonicity. The corpus only includes prosodic variations that listeners are able
to distinguish with reasonable accuracy, and we report these figures as a
benchmark against which text-to-speech prosody transfer can be compared.
  We conclude the paper with a demonstration of our proposed evaluation
methodology, using the corpus to evaluate two text-to-speech models that
perform prosody transfer.
