Thomas Drugman

Artificial Intelligence · 2012–2024

A researcher in the Science corpus: 15 papers; trusts 73 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 212 citations into it; #12,314 of 35,345 by trust here, #8,999 by citations
• Signal Processing — 127 citations into it; #2,364 of 7,572 by trust here, #1,255 by citations
• Speech & Audio — 103 citations into it; #482 of 1,076 by trust here, #613 by citations
• Natural Language Processing — 60 citations into it
• Generative Models — 53 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 15 citations into it

Whom Thomas Drugman trusts overall

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

1. Geoffrey E. Hinton
2. Dong Yu
3. Andreas Stolcke
4. Yoshua Bengio
5. Diederik P. Kingma
6. Alex Graves
7. Aäron van den Oord
8. Alex Krizhevsky
9. Yann LeCun
10. Ilya Sutskever

Who trusts Thomas Drugman overall

1. Amir Zadeh
2. Yang Ai
3. Percy Liang
4. Zhenhua Ling
5. Louis-philippe Morency
6. Soujanya Poria
7. Nishant Prateek
8. Xu Tan
9. Sri Karlapati

Papers from Thomas Drugman's perspective

Ordered by how strongly the researchers Thomas Drugman 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• COVAREP — A collaborative voice analysis repository for speech technologies (2014) — 834 citations
• BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data (2024) — 125 citations
• eCat: An End-to-End Model for Multi-Speaker TTS & Many-to-Many Fine-Grained Prosody Transfer (2023) — 3 citations
• Controllable Emphasis with zero data for text-to-speech (2023) — 6 citations
• A Comparative Analysis of Pretrained Language Models for Text-to-Speech (2023) — 3 citations
• Distribution Augmentation for Low-Resource Expressive Text-To-Speech (2022) — 10 citations
• CopyCat2: A Single Model for Multi-Speaker TTS and Many-to-Many Fine-Grained Prosody Transfer (2022) — 17 citations
• CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech (2020) — 74 citations
• Audio and Contact Microphones for Cough Detection (2020) — 36 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
• Towards Achieving Robust Universal Neural Vocoding (2018) — 96 citations
• Parameter Generation Algorithms for Text-To-Speech Synthesis with Recurrent Neural Networks (2018) — 5 citations
• Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models (2016) — 66 citations
• Train&align: A new online tool for automatic phonetic alignment (2012) — 34 citations