  Neural text-to-speech (TTS) synthesis can generate speech that is
indistinguishable from natural speech. However, the synthetic speech often
represents the average prosodic style of the database instead of having more
versatile prosodic variation. Moreover, many models lack the ability to control
the output prosody, which does not allow for different styles for the same text
input. In this work, we train a non-autoregressive parallel neural TTS
front-end model hierarchically conditioned on both coarse and fine-grained
acoustic speech features to learn a latent prosody space with intuitive and
meaningful dimensions. Experiments show that a non-autoregressive TTS model
hierarchically conditioned on utterance-wise pitch, pitch range, duration,
energy, and spectral tilt can effectively control each prosodic dimension,
generate a wide variety of speaking styles, and provide word-wise emphasis
control, while maintaining equal or better quality to the baseline model.
