  Neural sequence-to-sequence text-to-speech synthesis (TTS), such as
Tacotron-2, transforms text into high-quality speech. However, generating
speech with natural prosody still remains a challenge. Yasuda et. al. show that
unlike natural speech, Tacotron-2's encoder doesn't fully represent prosodic
features (e.g. syllable stress in English) from characters, and result in flat
fundamental frequency variations.
  In this work, we propose a novel carefully designed strategy for conditioning
Tacotron-2 on two fundamental prosodic features in English -- stress syllable
and pitch accent, that help achieve more natural prosody. To this end, we use
of a classifier to learn these features in an end-to-end fashion, and apply
feature conditioning at three parts of Tacotron-2's Text-To-Mel Spectrogram:
pre-encoder, post-encoder, and intra-decoder. Further, we show that jointly
conditioned features at pre-encoder and intra-decoder stages result in
prosodically natural synthesized speech (vs. Tacotron-2), and allows the model
to produce speech with more accurate pitch accent and stress patterns.
  Quantitative evaluations show that our formulation achieves higher
fundamental frequency contour correlation, and lower Mel Cepstral Distortion
measure between synthesized and natural speech. And subjective evaluation shows
that the proposed method's Mean Opinion Score of 4.14 fairs higher than
baseline Tacotron-2, 3.91, when compared against natural speech (LJSpeech
corpus), 4.28.
