Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction

@inproceedings{Zhao2020ImprovedPF,
  title={Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction},
  author={Yi Zhao and Haoyu Li and Cheng-I Lai and Jennifer Williams and Erica Cooper and Junichi Yamagishi},
  booktitle={INTERSPEECH},
  year={2020}
}
Vector Quantized Variational AutoEncoders (VQ-VAE) are a powerful representation learning framework that can discover discrete groups of features from a speech signal without supervision. Until now, the VQ-VAE architecture has previously modeled individual types of speech features, such as only phones or only F0. This paper introduces an important extension to VQ-VAE for learning F0-related suprasegmental information simultaneously along with traditional phone features.The proposed framework… 

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