Shinnosuke Takamichi

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 9 papers; trusts 85 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #10,956 of 35,345 by trust here, #12,580 by citations
• Signal Processing — 79 citations into it; #3,268 of 7,572 by trust here, #2,457 by citations
• Speech & Audio — 69 citations into it; #575 of 1,076 by trust here, #738 by citations
• Natural Language Processing — 25 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Optimization & Training — 9 citations into it
• Experimental and Cognitive Psychology — 6 citations into it

Whom Shinnosuke Takamichi trusts overall

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

1. Heiga Zen
2. Keiichi Tokuda
3. Hideki Kawahara
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Yoshua Bengio
7. Paul Taylor
8. Daniel W. Griffin
9. Alex Graves
10. Alex Krizhevsky

Who trusts Shinnosuke Takamichi overall

1. Hubert Siuzdak
2. Pallavi N. Baljekar
3. Berrak Sisman
4. William Grant Hatcher
5. Haitong Zhang
6. Yuki Saito
7. Ryuichi Yamamoto
8. Kasperi Palkama
9. Tomoya Yanagita
10. Sakriani Watiasri Sakti

Papers from Shinnosuke Takamichi's perspective

Ordered by how strongly the researchers Shinnosuke Takamichi 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Postfilters to Modify the Modulation Spectrum for Statistical Parametric Speech Synthesis (2016) — 66 citations
• RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesis (2024) — 44 citations
• Improving Speech Prosody of Audiobook Text-To-Speech Synthesis with Acoustic and Textual Contexts (2022) — 20 citations
• UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022 (2022) — 634 citations
• Disentangled Speaker and Language Representations Using Mutual Information Minimization and Domain Adaptation for Cross-Lingual TTS (2021) — 31 citations
• Cross-Lingual Speaker Adaptation Using Domain Adaptation and Speaker Consistency Loss for Text-To-Speech Synthesis (2021) — 12 citations
• Phase Reconstruction from Amplitude Spectrograms Based on Von-Mises-Distribution Deep Neural Network (2018) — 47 citations
• Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks (2017) — 213 citations
• JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis (2017) — 185 citations