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Speech recognition with deep recurrent neural networks
TLDR
This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs.
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
TLDR
This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Deep Neural Networks for Acoustic Modeling in Speech Recognition
TLDR
This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition.
Hybrid speech recognition with Deep Bidirectional LSTM
TLDR
The hybrid approach with DBLSTM appears to be well suited for tasks where acoustic modelling predominates, and the improvement in word error rate over the deep network is modest, despite a great increase in framelevel accuracy.
Acoustic Modeling Using Deep Belief Networks
TLDR
It is shown that better phone recognition on the TIMIT dataset can be achieved by replacing Gaussian mixture models by deep neural networks that contain many layers of features and a very large number of parameters.
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being
Convolutional Neural Networks for Speech Recognition
TLDR
It is shown that further error rate reduction can be obtained by using convolutional neural networks (CNNs), and a limited-weight-sharing scheme is proposed that can better model speech features.
Deep convolutional neural networks for LVCSR
TLDR
This paper determines the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks, and explores the behavior of neural network features extracted from CNNs on a variety of LVCSS tasks, comparing CNNs toDNNs and GMMs.
Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition
TLDR
The proposed CNN architecture is applied to speech recognition within the framework of hybrid NN-HMM model to use local filtering and max-pooling in frequency domain to normalize speaker variance to achieve higher multi-speaker speech recognition performance.
RobustFill: Neural Program Learning under Noisy I/O
TLDR
This work directly compares both approaches for automatic program learning on a large-scale, real-world learning task and demonstrates that the strength of each approach is highly dependent on the evaluation metric and end-user application.
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