An End-to-end Speech Recognition Algorithm based on Attention Mechanism

End-to-end speech recognition system is a major research field in speech recognition. The most typical model is the end-to-end speech recognition system based on CTC where RNN mining to and time sequence information are adopted, and a series of assumptions of HMM are discarded to obtain a good recognition rate. However, the CTC-based model is more dependent on the speech model and have a longer training cycle. Therefore, in the framework of traditional acoustic model, this paper proposes to train a feature extraction network of spectrogram based on attention mechanism by using prior knowledge. Firstly, it was spliced in the front end based on CTC model, and then the number of layers of cyclic neural network based on CTC model was reduced. Finally, it was combined to retrain. The experimental results show that the training time of the combined model is effectively reduced, and the accuracy of speech recognition is further improved.