• Corpus ID: 233204694

Flavored Tacotron: Conditional Learning for Prosodic-linguistic Features

@article{Elyasi2021FlavoredTC,
  title={Flavored Tacotron: Conditional Learning for Prosodic-linguistic Features},
  author={Mahsa Elyasi and Gaurav Bharaj},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.04050}
}
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. [1] 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… 

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