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Language recognition is an important branch of speech technology. As a front-end technology of speech information processing, higher recognition accuracy is required. It is found through research that there are obvious differences between the language maps of different languages, which can be used for language identification. This paper uses a convolutional neural network as a classification model, and compares the language recognition effects of traditional language recognition features anddoi:10.22158/jetss.v1n2p113 fatcat:ofe45tstf5gq7gbj3plhp6iwwa