Combining Deep De-noising Auto-encoder and Recurrent Neural Network in End-to-end Speech Recognition for Noise Robustness
碩士 === 國立中山大學 === 資訊工程學系研究所 === 106 === In this paper, we implement an end-to-end noise-robustness speech recognition system on Aurora 2.0 dataset through combining deep de-noising auto-encoders and recurrent neural networks. At front-end we use fully connected auto-encoder (FCDAE) to deal with nois...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/nrcpz2 |