Research on Acceleration Method of Speech Recognition Training.

Recurrent Neural Network (RNN) is now widely used in speech recognition. Experiments show that it has significant advantages over traditional methods, but complex computation limits its application, especially in real-time application scenarios. Recurrent neural network is heavily dependent on the pre- and post-state in calculation process, and there is much overlap information, so overlapping information can be reduced to accelerate training. This paper construct a training acceleration structure, which reduces the computation cost and accelerates training speed by discarding the dependence of pre-and poststate of RNN. Then correcting the recognition results errors with text corrector. We verify the proposed method on the TIMIT and Librispeech datasets, which prove that this approach achieves about 3 times speedup with little relative accuracy reduction.