Yoshihiko Nankaku

Artificial Intelligence · 2006–2020

A researcher in the Science corpus: 9 papers; trusts 34 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #10,937 of 35,345 by trust here, #8,283 by citations
• Signal Processing — 45 citations into it; #2,004 of 7,572 by trust here, #1,012 by citations
• Speech & Audio — 41 citations into it; #450 of 1,076 by trust here, #396 by citations
• Natural Language Processing — 27 citations into it; #4,680 of 5,688 by trust here, #5,502 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Generative Models — 14 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Yoshihiko Nankaku trusts overall

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

1. Keiichi Tokuda
2. Satoshi Imai
3. Tomoki Toda
4. Heiga Zen
5. Takao Kobayashi
6. Toshiaki Fukada
7. Geoffrey E. Hinton
8. Radford M. Neal
9. Takashi Masuko
10. David Talkin

Who trusts Yoshihiko Nankaku overall

1. Li Deng
2. Xu Tan
3. Keiichi Tokuda
4. Naihan Li
5. Brenden M. Lake
6. Chia-ying Lee
7. Shinnosuke Takamichi
8. Rui Liu
9. Heiga Zen
10. Jinglin Liu

Papers from Yoshihiko Nankaku's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Integration of Spectral Feature Extraction and Modeling for HMM-Based Speech Synthesis (2014) — 5 citations
• Redefining the Linguistic Context Feature Set for HMM and DNN TTS Through Position and Parsing (2016) — 11 citations
• Product of Experts for Statistical Parametric Speech Synthesis (2012) — 56 citations
• An HMM-based singing voice synthesis system (2006) — 141 citations
• Continuous Stochastic Feature Mapping Based on Trajectory HMMs (2011) — 73 citations
• Speech Synthesis Based on Hidden Markov Models (2013) — 468 citations
• Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis (2020) — 27 citations
• Singing Voice Synthesis Based on Generative Adversarial Networks (2019) — 59 citations
• Recent development of the HMM-based singing voice synthesis system - Sinsy (2010) — 100 citations