  Automatic speech recognition (ASR) tasks are resolved by end-to-end deep
learning models, which benefits us by less preparation of raw data, and easier
transformation between languages. We propose a novel end-to-end deep learning
model architecture namely cascaded CNN-resBiLSTM-CTC. In the proposed model, we
add residual blocks in BiLSTM layers to extract sophisticated phoneme and
semantic information together, and apply cascaded structure to pay more
attention mining information of hard negative samples. By applying both simple
Fast Fourier Transform (FFT) technique and n-gram language model (LM) rescoring
method, we manage to achieve word error rate (WER) of 3.41% on LibriSpeech test
clean corpora. Furthermore, we propose a new batch-varied method to speed up
the training process in length-varied tasks, which result in 25% less training
time.
