关键词: 序列级, 低资源, 端到端, 卷积神经网络, 批量归一化

Abstract: The cross-entropy criterion of mainstream neural network training is to classify and optimize each frame of acoustic data，while the continuous speech recognition uses the sequence-level transcription accuracy as a performance measure. In view of this difference, an end-to-end speech recognition system based on sequence level transcription is constructed in this paper.In order to solve the problem of poor system performance under the condition of low resource corpus,the model uses convolution neural network to deal with the input features, selects the best network structure, and performs two-dimensional convolution in the time and frequency domains thus improves the small disturbance influence caused by different environment and speaker in the input space. At the same time, neural network uses batch normalization technology to reduce generalization error and speed up training. Finally, based on the large language model, the hyper-parameters in decoding process are optimized to improve the modeling effect. Experimental results show that the system performance is improved by about 24%，better than mainstream speech recognition systems.

Key words: sequence level, low resource, end-to-end, convolution neural network, batch normalization