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![1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
“Parallel WaveNet: Fast High-Fidelity Speech Synthesis",
Google
Kei...](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-1-2048.jpg?cb=1666964470)
![⾳声合成の概要 towards End-to-End [Encoder-Decoder]
• DNN初期の⼿法は、⼊⼒（⾔語特徴量）と出
⼒（⾳響特徴量）の系列⻑を合わせるために
頑張る必要があった
• 最近の⼿法では、機械翻訳と同様の
Enc...](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-8-2048.jpg?cb=1666964470)
![⾳声合成の概要 towards End-to-End [波形⽣成]
• 伝統的な⾳声合成モデルでは、機械学習モデルは⾳響特徴量の予測までを担い、
「⾳響特徴量から⾳声波形の予測」は"Vocoder"と呼ばれるツールに任せていた
• Vocode...](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-9-2048.jpg?cb=1666964470)
![⾳声合成の概要 towards End-to-End [波形⽣成]
• WaveNetによる⽣成のイメージ
⾳声波形
今⽇は寒いですね
テキスト
⾳響特徴量
F0
外部モデル WaveNet
⾔語特徴量
⾳素
継続⻑](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-10-2048.jpg?cb=1666964470)
![Parallel Wavenet: Parallel WaveNet [Normalizing Flows]
• Normalizing Flows (Rezende+, 2015):
• 単純な多変量分布𝑝C(𝒛)を出発点とする(例えば、分散...](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-16-2048.jpg?cb=1666964470)
![Parallel Wavenet: Parallel WaveNet [IAF]
• Inverse Autoregressive Flows(Kingma+, 2017):
• 𝒙 = 𝑓(𝒛)を以下のように設計する
𝑥* = 𝑧* X 𝑠 ...](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-17-2048.jpg?cb=1666964470)
![Parallel Wavenet: Parallel WaveNet [Normalizing Flows + VAE]
• Normalizing Flows の使⽤例: VAE
• 通常VAEでは、事後分布を 𝑞l 𝒛 𝟎 𝒙 ~ 𝑁(𝜇 ...](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-18-2048.jpg?cb=1666964470)
![Parallel Wavenet: Parallel WaveNet [Normalizing Flows + ⽣成モデル]
• Normalizing Flowsは単体で⽣成モデルとして使うこともできる
• 以下の設定を考える
• 𝒛~𝐿𝑜𝑔...](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-19-2048.jpg?cb=1666964470)
![Parallel Wavenet: Parallel WaveNet [構造]
• WaveNetと同様のCausal + Dilated Convolution
• ⼊⼒: 𝐳~𝕃(0, 𝑰)
• 出⼒: 𝜇 𝒛,𝒕, 𝜽 , 𝑠 𝒛,𝒕,...](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-20-2048.jpg?cb=1666964470)
![Parallel Wavenet: Probability Density Distillation [追加的なloss]
• Probability Density Distillation にいくつかのLossを追加
• Power Los...](https://image.slidesharecdn.com/0105-180105000252/75/DL-Parallel-WaveNet-Fast-High-Fidelity-Speech-Synthesis-24-2048.jpg?cb=1666964470)
