Shinji Takaki

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 11 papers; trusts 188 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 155 citations into it; #3,512 of 35,345 by trust here, #5,390 by citations
• Signal Processing — 121 citations into it; #495 of 7,572 by trust here, #603 by citations
• Speech & Audio — 92 citations into it; #257 of 1,076 by trust here, #414 by citations
• Natural Language Processing — 55 citations into it; #1,261 of 5,688 by trust here, #2,634 by citations
• Computer Vision and Pattern Recognition — 38 citations into it
• Generative Models — 36 citations into it
• Model Architectures — 31 citations into it
• Optimization & Training — 15 citations into it

Whom Shinji Takaki trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Masanori Morise
2. Heiga Zen
3. Diederik P. Kingma
4. Alex Graves
5. Geoffrey E. Hinton
6. Keiichi Tokuda
7. Yoshua Bengio
8. Aäron van den Oord
9. Ilya Sutskever
10. Alex Krizhevsky

Who trusts Shinji Takaki overall

1. Xin Eric Wang
2. Takuma Okamoto
3. Junichi Yamagishi
4. Tomoki Toda
5. Hieu-Thi Luong
6. Lauri Juvela
7. Yang Ai
8. Yoshinori Shiga
9. Kainan Peng
10. Hyeong-Seok Choi

Papers from Shinji Takaki's perspective

Ordered by how strongly the researchers Shinji Takaki trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• A deep auto-encoder based low-dimensional feature extraction from FFT spectral envelopes for statistical parametric speech synthesis (2016) — 46 citations
• Neural Source-filter-based Waveform Model for Statistical Parametric Speech Synthesis (2018) — 132 citations
• Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech (2016) — 554 citations
• Neural Source-Filter Waveform Models for Statistical Parametric Speech Synthesis (2019) — 138 citations
• A Vector Quantized Variational Autoencoder (VQ-VAE) Autoregressive Neural $F_0$ Model for Statistical Parametric Speech Synthesis (2020) — 44 citations
• Investigation of Enhanced Tacotron Text-to-speech Synthesis Systems with Self-attention for Pitch Accent Language (2018) — 89 citations
• A Comparison of Recent Waveform Generation and Acoustic Modeling Methods for Neural-Network-Based Speech Synthesis (2018) — 69 citations
• Wasserstein GAN and Waveform Loss-Based Acoustic Model Training for Multi-Speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder (2018) — 67 citations
• Investigating different representations for modeling and controlling multiple emotions in DNN-based speech synthesis (2018) — 93 citations
• Adapting and controlling DNN-based speech synthesis using input codes (2017) — 77 citations
• Speech Enhancement for a Noise-Robust Text-to-Speech Synthesis System Using Deep Recurrent Neural Networks (2016) — 143 citations