Mixture Density Network for Phone-Level Prosody Modelling in Speech Synthesis

By Chenpeng Du, Kai Yu. 2021

8 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Chenpeng Du
2. K. Yu
3. Feiyu Shen
4. Yi Ren
5. Zhou Zhao
6. Xu Tan
7. Ming Lei
8. Zhiying Huang

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, neutral): “Expressive Modeling Variation Information GST-Tacotron [377], Ref-Tacotron [303], [39] Multi-SpectroGAN [182], PriorGrad [181], [48] VAE-TTS [436], Glow-TTS [156], [116, 117] [99, 4, 360, 100, 318, 319, 360, 68, 138]”
• Acoustic Word Embeddings for End-to-End Speech Synthesis (2021, methodology): “The TTS models in this work are based on FastSpeech2 [5] with GMM-based prosody modeling [13].”
• Prosospeech: Enhancing Prosody with Quantized Vector Pre-Training in Text-To-Speech (2022, background): “…to learn latent disentangled representations and successfully factorize speaker identity and prosody; prosody prediction-based methods [5, 14] first extract prosody attributes including pitch, duration and energy and use some modules to predict them conditioned on the input linguistic…”