  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.
