SE-MCNN-CTC的中文语音识别声学模型

针对国内外缺少对振动轮噪声预估的问题,以某型振动轮为研究对象,首先基于动力学有限元理论对振动轮进行频率响应分析,其次采用声学边界元技术对振动轮辐射噪声进行了数值模拟,并通过实验验证了仿真结果的准确性,然后比较了垂直振动与圆周振动两种不同激振形式对辐射噪声的影响,得出垂直振动辐射噪声低的结论,最后对驾驶室声腔模态进行了仿真,与振动轮激振频率相近发生共振。通过调整激振频率,降低了司机耳旁噪声。所得研究成果可为振动轮辐射噪声的预估与改进提供一种切实可行的参考依据。

Towards end-to-end speech recognition for Chinese mandarin using SE-MCNN-CTC

In order to solve the problems of high prediction error rate and poor generalization performance with traditional Convolutional Neural Network in Chinese speech recognition, different convolutional layers, pooling layers and fully connected layers on DCNN-CTC are analyzed in this paper. Based on the above model, two kinds of acoustic models referred as MCNN-CTC and SE-MCNN-CTC are proposed, respectively. With the combination of the advantages of MCNN and SENet in the latter model, the deep information transmission is reinforced, and the gradient problems can be effectively avoided simultaneously, the extracted feature maps can be adaptively recalibrated. Compared with DCNN-CTC, the research results show that SE-MCNN-CTC not only yields a 13.51% relative PER reduction, and the final PER is 22.21%, but also the generalization performance of the improved acoustic model can be improved effectively.