Jaime Lorenzo-Trueba

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 10 papers; trusts 36 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 131 citations into it; #7,309 of 35,345 by trust here, #6,956 by citations
• Signal Processing — 105 citations into it; #1,374 of 7,572 by trust here, #853 by citations
• Speech & Audio — 85 citations into it; #925 of 1,076 by trust here, #923 by citations
• Natural Language Processing — 39 citations into it
• Generative Models — 38 citations into it; #1,697 of 1,956 by trust here, #1,715 by citations
• Computer Vision and Pattern Recognition — 32 citations into it

Whom Jaime Lorenzo-Trueba trusts overall

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

1. Aäron van den Oord
2. Hideki Kawahara
3. Diederik P. Kingma
4. Gautham J. Mysore
5. Geoffrey E. Hinton
6. Nal Kalchbrenner
7. Masanori Morise
8. Karen Simonyan
9. Alex Graves
10. Heiga Zen

Who trusts Jaime Lorenzo-Trueba overall

1. Yang Ai
2. Xin Eric Wang
3. Junichi Yamagishi
4. Zhenhua Ling
5. Mohammed Salah Al-Radhi
6. Xu Tan
7. Hirokazu Kameoka
8. Lauri Juvela
9. Bastian Schnell
10. Wen-Chin Huang

Papers from Jaime Lorenzo-Trueba's perspective

Ordered by how strongly the researchers Jaime Lorenzo-Trueba 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods (2018) — 351 citations
• Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations (2024) — 9 citations
• Low-Resource Expressive Text-To-Speech Using Data Augmentation (2020) — 65 citations
• Using Vaes and Normalizing Flows for One-Shot Text-To-Speech Synthesis of Expressive Speech (2019) — 33 citations
• In Other News: a Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data (2019) — 30 citations
• Robust universal neural vocoding (2018) — 19 citations
• High-Quality Nonparallel Voice Conversion Based on Cycle-Consistent Adversarial Network (2018) — 144 citations
• Towards Achieving Robust Universal Neural Vocoding (2018) — 96 citations
• A Comparison of Recent Waveform Generation and Acoustic Modeling Methods for Neural-Network-Based Speech Synthesis (2018) — 69 citations
• Investigating different representations for modeling and controlling multiple emotions in DNN-based speech synthesis (2018) — 93 citations