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About Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks
This document describes a deep neural network architecture that combines convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and fully connected deep neural networks (DNNs). CNNs are used to reduce frequency variations in the input, LSTMs are used for temporal modeling, and DNNs map the features into a more separable space. The proposed convolutional long short-term memory deep neural network (CLDNN) architecture is evaluated on various large vocabulary speech recognition tasks and is found to outperform LSTMs alone by 4-6%.
Detailed Information
| Author: | ['yacov'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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