Alan McCree
Artificial Intelligence · 1995–2019
A researcher in the Science corpus: 8 papers; trusts 14 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 40 citations into it; #18,878 of 35,345 by trust here, #14,438 by citations
- Signal Processing — 37 citations into it; #3,983 of 7,572 by trust here, #3,673 by citations
- Speech & Audio — 27 citations into it; #776 of 1,076 by trust here, #628 by citations
- Computer Vision and Pattern Recognition — 5 citations into it
- Natural Language Processing — 5 citations into it
Whom Alan McCree trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David Snyder
- Daniel Garcia-Romero
- Patrick Kenny
- Daniel Povey
- Najim Dehak
- Sergey Ioffe
- Florin Curelaru
- Simon J. D. Prince
- Sanjeev Khudanpur
- Arsha Nagrani
Who trusts Alan McCree overall
- Yannis Agiomyrgiannakis
- Rainer Martin
- Keiichi Tokuda
- Xin Eric Wang
- Reo Yoneyama
- Felicia S. C. Lim
- Jan Skoglund
- W. Kleijn
- Junichi Yamagishi
- Heiga Zen
Papers from Alan McCree's perspective
Ordered by how strongly the researchers Alan McCree trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- The Kaldi Speech Recognition Toolkit
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Front-End Factor Analysis for Speaker Verification
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Improving speaker recognition performance in the domain adaptation challenge using deep neural networks (2014) — 97 citations
- The NIST 2014 Speaker Recognition i-vector Machine Learning Challenge (2014) — 108 citations
- Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge (2018) — 234 citations
- Speaker Recognition for Multi-speaker Conversations Using X-vectors (2019) — 322 citations
- Spoken Language Recognition using X-vectors (2018) — 246 citations
- The MITLL NIST LRE 2007 language recognition system (2008) — 41 citations
- A 2.4 kbit/s MELP coder candidate for the new U.S. Federal Standard (1996)
- A mixed excitation LPC vocoder model for low bit rate speech coding (1995)