Open AccessPosted Content
Deep Speech: Scaling up end-to-end speech recognition
Awni Hannun,Carl Case,Jared Casper,Bryan Catanzaro,Greg Diamos,Erich Elsen,Ryan Prenger,Sanjeev Satheesh,Shubho Sengupta,Adam Coates,Andrew Y. Ng +10 more
TL;DR: Deep Speech, a state-of-the-art speech recognition system developed using end-to-end deep learning, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set.
read more
Abstract: We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a "phoneme." Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Posted Content
Multi-Modal Data Augmentation for End-to-end ASR
TL;DR: In this paper, a multi-modal data augmentation network (MMDA) is proposed to combine acoustic and symbolic input for ASR, which enables seamless mixing of large text datasets with significantly smaller transcribed speech corpora during training.
33
From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review
TL;DR: The results show that, despite the limitations of the method, it is possible to identify fast-growing areas of research regardless of the number of articles, and predict publication activity in the short term with satisfactory accuracy for practice.
33
Optimizing Reconfigurable Recurrent Neural Networks
Zhiqiang Que,Hiroki Nakahara,Eriko Nurvitadhi,Hongxiang Fan,Chenglong Zeng,Jiuxi Meng,Xinyu Niu,Wayne Luk +7 more
- 03 May 2020
TL;DR: A novel latency-hiding hardware architecture based on column-wise matrix-vector multiplication to eliminate data dependency is proposed, improving the throughput of systems of RNN models and a flexible checkerboard tiling strategy is introduced to allow large weight matrices.
33
Adversarial Deep Learning: A Survey on Adversarial Attacks and Defense Mechanisms on Image Classification
01 Jan 2022
TL;DR: A comprehensive review of the most recent and state-of-the-art adversarial attack methods by providing an in-depth analysis and explanation of the working process of these attacks is provided in this article .
Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report.
TL;DR: The impact of AI through the entire imaging cycle of musculoskeletal radiology, from the placement of the requisition to the generation of the report, is explored, with an added Canadian perspective.
33
References
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
- 07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
•Proceedings Article
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever,Oriol Vinyals,Quoc V. Le +2 more
- 08 Dec 2014
TL;DR: The authors used a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
•Proceedings Article
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
- 21 Jun 2010
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.
Backpropagation applied to handwritten zip code recognition
Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
12.5K
Related Papers (5)
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014