  Modern neural text-to-speech (TTS) synthesis can generate speech that is
indistinguishable from natural speech. However, the prosody of generated
utterances often represents the average prosodic style of the database instead
of having wide prosodic variation. Moreover, the generated prosody is solely
defined by the input text, which does not allow for different styles for the
same sentence. In this work, we train a sequence-to-sequence neural network
conditioned on acoustic speech features to learn a latent prosody space with
intuitive and meaningful dimensions. Experiments show that a model conditioned
on sentence-wise pitch, pitch range, phone duration, energy, and spectral tilt
can effectively control each prosodic dimension and generate a wide variety of
speaking styles, while maintaining similar mean opinion score (4.23) to our
Tacotron baseline (4.26).
