Cassia Valentini-Botinhao

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 8 papers; trusts 87 researchers and is trusted by 76 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #3,150 of 35,345 by trust here, #8,077 by citations
• Signal Processing — 86 citations into it; #348 of 7,572 by trust here, #933 by citations
• Speech & Audio — 54 citations into it; #144 of 1,076 by trust here, #587 by citations
• Natural Language Processing — 27 citations into it
• Computational Mechanics — 16 citations into it
• Model Architectures — 16 citations into it
• Cognitive Neuroscience — 14 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it

Whom Cassia Valentini-Botinhao trusts overall

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

1. Keiichi Tokuda
2. Israel Cohen
3. Hideki Kawahara
4. Geoffrey E. Hinton
5. Junichi Yamagishi
6. Tomoki Toda
7. David Talkin
8. Heiga Zen
9. Alex Graves
10. Felix Weninger

Who trusts Cassia Valentini-Botinhao overall

1. Chen Zhang
2. Alexandre D'efossez
3. Joan Serrà
4. Santiago Pascual
5. Yi-Chiao Wu
6. Junichi Yamagishi
7. Szu-Wei Fu
8. Janusz Klejsa
9. Lauri Juvela
10. Jing Shi

Papers from Cassia Valentini-Botinhao's perspective

Ordered by how strongly the researchers Cassia Valentini-Botinhao 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Noisy speech database for training speech enhancement algorithms and TTS models (2017) — 241 citations
• Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech (2016) — 554 citations
• Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis (2015) — 270 citations
• Speech Enhancement of Noisy and Reverberant Speech for Text-to-Speech (2018) — 36 citations
• Speech Enhancement for a Noise-Robust Text-to-Speech Synthesis System Using Deep Recurrent Neural Networks (2016) — 143 citations
• A Deep Generative Architecture for Postfiltering in Statistical Parametric Speech Synthesis (2015) — 50 citations
• DNN-based stochastic postfilter for HMM-based speech synthesis (2014) — 41 citations
• Edinburgh Research Explorer Modelling acoustic feature dependencies with artificial neural networks: Trajectory-RNADE