摘要： 地铁交通运营是一种整体性活动，离不开各部门间的协调配合,地铁运营岗位应急处置培训系统应用于多个岗位的联合培训。该系统通过语音识别来实现模拟岗位间语音交互及对培训过程智能评价的功能。提出的语音识别方法可实现离线网络下对地铁培训专业术语的高识别精度，利用深度全序列卷积神经网络（DFCNN）和链接时序分类（CTC）构建声学模型，对应急处置培训用语进行整理并构建专业术语库，基于统计学构建语言模型。实验结果表明，该语音识别方法能够有效识别地铁应急处置培训用语，为地铁运营岗位人员的培训和考核提供更全面的评价指标。

中图分类号： U231.92；TP39
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2022.06.012
引用格式： 周杨，钱雪军. 地铁运营岗位应急处置培训的语音识别研究[J].信息技术与网络安全，2022，41(6)：73-76，93.

Speech recognition research on emergency disposal training for subway operation positions

Abstract： Metro transportation operation is a holistic activity, and it is inseparable from the coordination and cooperation between various departments. The subway operation post emergency response training system is used for joint training of multiple posts. The system needs voice recognition to simulate the function of voice interaction between posts and intelligent evaluation of the training process. The speech recognition method proposed in this paper realizes high recognition accuracy of subway training terminology under offline network. Using deep fully convolutional neural network (DFCNN) and connectionist temporal classification (CTC) technology to build acoustic models, collating and constructing professional terminology bases for emergency disposal training terms, and building language models based on statistics and terminology banks. Experimental results show that the speech recognition technology can effectively identify training terms and provide more comprehensive evaluation indicators for the training and assessment of personnel in operational positions.

Key words : metro emergency disposal training; speech recognition; convolutional neural networks