Abstract: In order to promote the application of speech recognition for railway passenger services, a study on speech recognition model for railway passenger service applications is made, in which the Conformer encoder structure based on convolution enhancement and the RNN-Transducer model structure are used to realize the Conformer-Transducer speech recognition model. Since the convolution neural networks tend to ignore the association between the whole signal and a signal sequence, the convolution module in the Conformer structure are improved and the attention mechanism is added to the convolution module for modifying the calculation results of the convolution module. A speech data set of railway passenger service is built to test and evaluate the improved model and the results show that the accuracy of the improved speech recognition model can reach 92.09% and the error rate of speech recognition is reduced by 0.33% compared with the general Conformer-Transducer model. Because railway passenger services involves specific text information, such as railway travel regulations and frequently asked questions by the passengers, a text processing mechanism, language model or weighting of hot words, is then integrated into the speech recognition model, which enable the model recognize railway-specific terms better than other speech recognition algorithims. This speech recognition model has been applied in passenger FAQ inquiry equipment and intelligent station service robot, which is conducive not only to enhance the level of railway passenger services and improve railway passenger travel experience but also to facilitate downsizing the staff and increasing the work efficiency of railway passenger service.