Search Results for author:
Found 10 papers, 5 papers with code
WaveNet: A Generative Model for Raw Audio
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
Ranked #1 on
Speech Synthesis
on Mandarin Chinese
Latent Predictor Networks for Code Generation
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs.
Ranked #10 on
Code Generation
on Django
Deep Audio-Visual Speech Recognition
The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio.
Ranked #6 on
Audio-Visual Speech Recognition
on LRS2
Audio-Visual Speech Recognition
Automatic Speech Recognition (ASR)
+4
Lip Reading Sentences in the Wild
The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio.
Ranked #4 on
Lipreading
on GRID corpus (mixed-speech)
(using extra training data)
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
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)
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition
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.
Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition
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.
Large-Scale Visual Speech Recognition
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 #11 on
Lipreading
on LRS3-TED
(using extra training data)
Large Scale Distributed Deep Networks
Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance.
Deep Neural Networks for Acoustic Modeling in Speech Recognition
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.
