Search Results for author: Andrew Senior

Found 10 papers, 3 papers with code

Large-Scale Visual Speech Recognition

no code implementations ICLR 2019 Brendan Shillingford, Yannis Assael, Matthew W. Hoffman, Thomas Paine, Cían Hughes, Utsav Prabhu, Hank Liao, Hasim Sak, Kanishka Rao, Lorrayne Bennett, Marie Mulville, Ben Coppin, Ben Laurie, Andrew Senior, Nando de Freitas

To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3, 886 hours of video).

Ranked #7 on Lipreading on LRS3-TED (using extra training data)

Lipreading Visual Speech Recognition

Lip Reading Sentences in the Wild

no code implementations CVPR 2017 Joon Son Chung, Andrew Senior, Oriol Vinyals, Andrew Zisserman

The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio.

Ranked #3 on Lipreading on GRID corpus (mixed-speech) (using extra training data)

Lipreading Lip Reading +1

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

no code implementations NeurIPS 2016 Jack W. Rae, Jonathan J. Hunt, Tim Harley, Ivo Danihelka, Andrew Senior, Greg Wayne, Alex Graves, Timothy P. Lillicrap

SAM learns with comparable data efficiency to existing models on a range of synthetic tasks and one-shot Omniglot character recognition, and can scale to tasks requiring $100,\! 000$s of time steps and memories.

Ranked #6 on Question Answering on bAbi (Mean Error Rate metric)

Language Modelling Machine Translation +2

Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition

no code implementations24 Jul 2015 Haşim Sak, Andrew Senior, Kanishka Rao, Françoise Beaufays

We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition.

General Classification Speech Recognition

Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition

no code implementations5 Feb 2014 Haşim Sak, Andrew Senior, Françoise Beaufays

However, in contrast to the deep neural networks, the use of RNNs in speech recognition has been limited to phone recognition in small scale tasks.

Handwriting Recognition Speech Recognition

Large Scale Distributed Deep Networks

no code implementations NeurIPS 2012 Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le, Andrew Y. Ng

Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance.

Object Recognition Speech Recognition

Deep Neural Networks for Acoustic Modeling in Speech Recognition

no code implementations Signal Processing Magazine 2012 Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input.

Speech Recognition

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