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The first step in any voice recognition software is to determine whatlanguage a speaker is using, and ideally this process would be automated. Thetechnique described in this paper, language identification for audiospectrograms (LIFAS), uses spectrograms generated from audio signals as inputsto a convolutional neural network (CNN) to be used for language identification.LIFAS requires minimal pre-processing on the audio signals as the spectrogramsare generated during each batch as they are input to the network duringtraining.  LIFAS utilizes deep learning tools that are shown to be successful on imageprocessing tasks and applies it to audio signal classification. LIFAS performsbinary language classification with an accuracy of 97\%, and multi-classclassification with six languages at an accuracy of 89\% on 3.75 second audioclips.

Multiclass Language Identification using Deep Learning on Spectral Images of Audio Signals