关键词: 卷积神经网络, 递归神经网络, 声学模型, 端到端模型

Abstract: End-to-end neural networks can automatically learn feature transformation from original data, which can solve the mismatch between hand designed features and specific tasks. The traditional end-to-end neural network for speech recognition uses a time domain convolution network as the feature extraction model, recurrent neural network and full connected feed-forwarddeep neural network as the acoustic model, which has some limitations in performance and efficiency. From the aspects of the performanceof thefeature extraction module and the training efficiency of the acoustic model, an end-to-end speech recognition model combining the multi-time and frequency resolution convolution and the feed-forward neural network with memory modules is proposed. On the real recording test dataset, the proposed method reduces the word error rate by 10%, training time by 80% compared with the traditional method.

Key words: convolution neural network, recurrent neural network, acoustic model, end-to-end neural network