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Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR
October 12, 2017 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Dan Lim
arXiv ID
1710.04515
Category
cs.CL: Computation & Language
Citations
2
Venue
arXiv.org
Last Checked
5 months ago
Abstract
This thesis introduces the sequence to sequence model with Luong's attention mechanism for end-to-end ASR. It also describes various neural network algorithms including Batch normalization, Dropout and Residual network which constitute the convolutional attention-based seq2seq neural network. Finally the proposed model proved its effectiveness for speech recognition achieving 15.8% phoneme error rate on TIMIT dataset.
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