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8 次 は 新金岡 新金岡 です 名詞 助詞 固有名詞 固有名詞 助動詞 ツギ ワ シンカナオカ シンカナオカ デス * * * ツギ ワ シンカナオカ シンカナオカ デス * * * DNN T frames 8
9 9
10 この部分を見てみる 10
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14 Synthesis filter 14
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17 Speech frames Spectral features unvoiced unvoiced 200 Hz F0 value T frames 17
18 次 は 新金岡 新金岡 です 名詞 助詞 固有名詞 固有名詞 助動詞 ツギ ワ シンカナオカ シンカナオカ デス * * * ツギ ワ シンカナオカ シンカナオカ デス * * * DNN T frames 18
19 各フレームでの処理を見ると Heiga Zen, Andrew Senior, Mike Schuster, Statistical Parametric Speech Synthesis Using Deep Neural Networks, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP),
20 全体での処理 (FeedForward 型の例 ) unvoiced unvoiced 200 Hz 205 Hz 210 Hz 220 Hz T frames 位置 :1 つ目 2 つ目 3 つ目 4 つ目 5 つ目 6 つ目 7 つ目 20
21 FF Highway block FF FF FF X + FF FF X -1 X Xin Wang, Shinji Takaki, Junichi Yamagishi, "Investigating very deep highway networks for parametric speech synthesis", Speech Communication
22 22
23 female voice male voice 23 Xin Wang, Shinji Takaki, Junichi Yamagishi, "A Comparative Study of the Performance of HMM, DNN, and RNN based Speech Synthesis Systems Trained on Very Large Speaker-Dependent Corpora", 9th ISCA Workshop on Speech Synthesis
24 24
25 kHz16,000 - AR - AR : LPC 25
26 - - 26
27 Xin Wang, Shinji Takaki, Junichi Yamagishi, "AN AUTO REGRESSIVE RECURRENT MIXTURE DENSITY NETWORK FOR PARAMETRIC SPEECH SYNTHESIS", icassp Xin Wang, Shinji Takaki, Junichi Yamagishi, "An RNN-based Quantized F0 Model with Multi-tier Feedback Links fortext-to-speech Synthesis", Interspeech
28 1-D CNNs Softmax Quantized waveform + Block 1 Block 2 Block 40 1-D CNN 1-D CNN 1-D CNN 1-D CNN + 1-D CNN + 1-D CNN + * * * Tanh Sigmoid Tanh Sigmoid Tanh Sigmoid Dilated 1-D CNN Dilated 1-D CNN Dilated 1-D CNN Feedforward Up sampling Time resolution: 16kHz One-hot quantized waveform (time shifted) Conditional Parameters Feedforward Bi-LSTM Time resolution: 1/(5ms) = 20Hz (Frame level) van den Oord, Aaron; Dieleman, Sander; Zen, Heiga; Simonyan, Karen; Vinyals, Oriol; Graves, Alex; Kalchbrenner, Nal; Senior, Andrew; Kavukcuoglu, Koray, WaveNet: A Generative Model for Raw Audio, Arxiv
29 + 1-D CNN Softmax Waveform 1-D CNN 1-D CNN + 1-D CNN 1-D CNN + 1-D CNN 1-D CNN + Tanh * + Sigmoid Tanh * + Sigmoid Tanh * + Sigmoid Diluted 1-D CNN Diluted 1-D CNN Diluted 1-D CNN 1-D CNN Linear Waveform (time shifted) F0 Bi-directional LSTM Spectral features Neural Waveform Generator (16kHz) Hierarchical-softmax Linear Autoregressive GMM Linear Uni-directional LSTM Bi-directional LSTM Autoregressive Acoustic Models (200Hz) Bi-directional LSTM Tanh-feedforward Tanh-feedforward Linguistic features Bi-directional LSTM Tanh-feedforward Tanh-feedforward 29
30 Xin Wang, Jaime Lorenzo-Trueba, Shinji Takaki, Lauri Juvela, Junichi Yamagishi "A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis", ICASSP SAR-Wa SAR-Pr SAR-Pm SAR-Wo SGA-Wo RGA-Wo RNN-Wo Phase recovery minimum phase Wavenet Wavenet PML WORLD Waveform g e n e r a t o r s GAN F0 MGC GAN DAR SAR RNN A c o u s t i c models Linguistic features Reference :16kHz :48kHz Wavenet 30
31 TTS Hieu-Thi Luong, Xin Wang, Junichi Yamagishi, Nobuyuki Nishizawa "Do prosodic manual annotations matter for Japanese speech synthesis systems with WaveNet vocoder? Submitted to Interspeech
32 The cat in the hat 32
33 Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. "Sequence to sequence learning with neural networks." Advances in neural information processing systems
34 Spectrogram J. Shen, M. Schuster, N. Jaitly, R. Skerry-Ryan, R. A. Saurous, R. J. Weiss, R. Pang, Y. Agiomyrgiannakis, Y. Wu, Y. Zhang, Y. Wang, Z. Chen, and Z. Yang, Natural TTS synthesis by conditioning wavenet on mel spectrogram predictions, ICASSP
35 - - Deep Voice3 from Baidu (Tacotron2 + dot-product attention + speaker embedding) 35 Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan O. Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, John Miller, Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning ICLR 2018
36 36
37 37
38 The cat in the hat 38
39 sil a i sil (khz) 39
40 /a/ 0 /i/ 0.2 /u/ 0 /e/ 0.3 /o/ 0.5 /a/ 0 /i/ 0.1 /u/ 0 /e/ 0.4 /o/ 0.5 /a/ 0 /i/ 0 /u/ 0 /e/ 0.5 /o/ 0.3 /a/ 0.3 /i/ 0 /u/ 0 /e/ 0.5 /o/ 0.2 /a/ 0.45 /i/ 0 /u/ 0 /e/ 0.35 /o/ 0.2 /a/ 0.55 /i/ 0 /u/ 0 /e/ 0.3 /o/ 0.2 Acoustic sequence 40
41 /a/ 0 /i/ 0.2 /u/ 0 /e/ 0.3 /o/ 0.5 / / 0 /a/ 0 /i/ 0.1 /u/ 0 /e/ 0.4 /o/ 0 / / 0.5 /a/ 0 /i/ 0 /u/ 0 /e/ 0.5 /o/ 0.3 / / 0 /a/ 0.3 /i/ 0 /u/ 0 /e/ 0 /o/ 0.2 / / 0.5 /a/ 0.45 /i/ 0 /u/ 0 /e/ 0.35 /o/ 0.2 / / 0 /a/ 0 /i/ 0 /u/ 0 /e/ 0.3 /o/ 0.2 / / 0.55 Acoustic sequence 41
42 42
43 Bi-directional RNN Convolution Spectrogram 43
44 Test set Deep speech 2 Human WSJ eval WSJ eval LibriSpeech test-clean LibriSpeech test-other Amodei, Dario, et al. "Deep speech 2: End-to-end speech recognition in english and mandarin." arxiv preprint arxiv: (2015). 44
45 /but/ 0.2 /cat/ 0.5 /hat/ 0.2 /and/ 0.1 / / 0 word pieces Previously predicted word /a/ 0 /an/ 0.2 /the/ 0.5 /its/ 0.3 / / 0 Language model Acoustic model Kanishka Rao, Haşim Sak, Rohit Prabhavalkar, Exploring Architectures, Data and Units For Streaming End-to-End Speech Recognition with RNN-Transducer ASRU
46 DNN break thorough Switchboard WER 10 Human
47 47
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