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Automatic speech recognition (ASR) tasks are resolved by end-to-end deeplearning models, which benefits us by less preparation of raw data, and easiertransformation between languages. We propose a novel end-to-end deep learningmodel architecture namely cascaded CNN-resBiLSTM-CTC. In the proposed model, weadd residual blocks in BiLSTM layers to extract sophisticated phoneme andsemantic information together, and apply cascaded structure to pay moreattention mining information of hard negative samples. By applying both simpleFast Fourier Transform (FFT) technique and n-gram language model (LM) rescoringmethod, we manage to achieve word error rate (WER) of 3.41% on LibriSpeech testclean corpora. Furthermore, we propose a new batch-varied method to speed upthe training process in length-varied tasks, which result in 25% less trainingtime.

Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition