Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR

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.

Masters thesis, Korea Univ

References in corpus (3)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Residual Convolutional CTC Networks for Automatic Speech Recognition
• Very Deep Convolutional Networks for End-to-End Speech Recognition